Data Science models
import pandas as pd
import numpy as np
import seaborn as sns
import missingno as msno
import yfinance as yf
import matplotlib.pyplot as plt
import math
import statsmodels.api as sm
from scipy.stats import norm
import scipy.optimize as opt
import datetime
import time
from sklearn.metrics import mean_squared_error as mse
from arch import arch_model
from statsmodels.tsa.stattools import ARMA
from statsmodels.tsa.arima_model import ARIMA
from time import time
import sys
pd.options.display.max_rows = 10
import datetime
from matplotlib.pyplot import figure
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
from tensorflow.keras import layers
from numpy import asarray
from pandas import DataFrame
from pandas import concat
from sklearn.metrics import mean_absolute_error
from sklearn.ensemble import RandomForestRegressor
from matplotlib import pyplot
from sklearn.metrics import mean_squared_error
from math import sqrt
from tabulate import tabulate
import statsmodels.tsa.api as tsa
import matplotlib.cm as cm
from matplotlib.pyplot import figure
import warnings
warnings.filterwarnings("ignore")
We use data from four international stock markets, including two different industries (Banking and Automotive) retrieved from YahooFinance from the period 2000-01-01 to 2020-12-31.
#Let's read the data for the chosen period (2000-01-01 --> 2020-12-31)
Tickers = ["DBK.DE", "BAC", "BMW.DE", "F"]
df= yf.download(Tickers, start = "2000-01-01", end = "2020-12-31") ['Close']
df.columns = df.columns.str.lower().str.replace(' ', '_')
df
[*********************100%***********************] 4 of 4 completed
| bac | bmw.de | dbk.de | f | |
|---|---|---|---|---|
| Date | ||||
| 1999-12-31 | 25.093750 | NaN | NaN | 29.297592 |
| 2000-01-03 | 24.218750 | 29.490000 | 65.045204 | 28.782394 |
| 2000-01-04 | 22.781250 | 28.299999 | 61.775078 | 27.820692 |
| 2000-01-05 | 23.031250 | 27.740000 | 60.975021 | 27.923731 |
| 2000-01-06 | 25.000000 | 27.650000 | 64.082031 | 27.958078 |
| ... | ... | ... | ... | ... |
| 2020-12-23 | 30.049999 | 73.379997 | 8.983000 | 8.990000 |
| 2020-12-24 | 29.959999 | NaN | NaN | 8.860000 |
| 2020-12-28 | 30.129999 | 73.489998 | 9.218000 | 8.890000 |
| 2020-12-29 | 30.010000 | 73.160004 | 8.944000 | 8.820000 |
| 2020-12-30 | 29.980000 | 72.230003 | 8.949000 | 8.860000 |
5438 rows × 4 columns
#Let's calculate the "Daily log returns"
df[['Ret_BAC', 'Ret_BMW', 'Ret_DBK', 'Ret_F']]=np.log(df[['bac','bmw.de', 'dbk.de', 'f']]).diff()
df = df.dropna() #Deleting missing values
#Converting the table as a Data Frame to be more practical
df = pd.DataFrame(df)
#Keeping only the retuns columns
df_RET = df[['Ret_BAC', 'Ret_BMW', 'Ret_DBK', 'Ret_F']]
df_RET
| Ret_BAC | Ret_BMW | Ret_DBK | Ret_F | |
|---|---|---|---|---|
| Date | ||||
| 2000-01-04 | -0.061189 | -0.041189 | -0.051582 | -0.033984 |
| 2000-01-05 | 0.010914 | -0.019986 | -0.013036 | 0.003697 |
| 2000-01-06 | 0.082024 | -0.003250 | 0.049700 | 0.001229 |
| 2000-01-07 | -0.026601 | -0.001810 | 0.029853 | 0.071120 |
| 2000-01-10 | -0.035275 | 0.039081 | -0.026102 | -0.018476 |
| ... | ... | ... | ... | ... |
| 2020-12-21 | 0.036642 | -0.030380 | -0.045192 | -0.002237 |
| 2020-12-22 | -0.017982 | 0.001805 | 0.017799 | -0.015802 |
| 2020-12-23 | 0.028352 | 0.017597 | 0.022174 | 0.022498 |
| 2020-12-29 | -0.003991 | -0.004500 | -0.030175 | -0.007905 |
| 2020-12-30 | -0.001000 | -0.012793 | 0.000559 | 0.004525 |
5008 rows × 4 columns
#Calculating the "Realized Volatility" using the close-to-close method
df_Vol = df_RET.std()* 252 ** 0.5 #252 are the trading days
df_Vol
Ret_BAC 0.457025 Ret_BMW 0.334252 Ret_DBK 0.411080 Ret_F 0.419874 dtype: float64
# Prepare the Data frame for each company
## DBK
DBK_RET = df[['Ret_DBK']]*100 #*100 because the GARCH fitting process has better convergence if we express the returns as percents
##BAC
BAC_RET = df[['Ret_BAC']] * 100
##BMW
BMW_RET = df[['Ret_BMW']] * 100
##Ford
F_RET = df[['Ret_F']] * 100
# Plotting DBK Price
fig = plt.figure()
plt.figure(figsize=(20, 7))
plt.plot(df[["dbk.de"]], label = ('DBK historical prices'))
plt.legend(loc='upper right')
plt.title('DBK: historical prices', fontsize=16)
plt.show()
# Plotting DBK Returns
fig = plt.figure()
plt.figure(figsize=(20, 7))
plt.plot(df[["Ret_DBK"]], label = ('BAC historical prices'))
plt.legend(loc='upper right')
plt.title('DBK: Daily returns', fontsize=16)
plt.show()
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
# Plotting BAC Price
fig = plt.figure()
plt.figure(figsize=(20, 7))
plt.plot(df[["bac"]], label = ('BAC historical prices'))
plt.legend(loc='upper right')
plt.title('BAC: historical prices', fontsize=16)
plt.show()
# Plotting BAC Returns
fig = plt.figure()
plt.figure(figsize=(20, 7))
plt.plot(df[["Ret_BAC"]], label = ('BAC historical prices'))
plt.legend(loc='upper right')
plt.title('BAC: Daily returns', fontsize=16)
plt.show()
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
We can apply the same analysis to the BAC case. However, in the United States, we can judge that the effects of the 2003 financial crisis were minimal. Indeed, we can see that prices continued to rise during this period. The same is true for the 2011-2012 period. The two major events where we notice a strong decrease in prices, as well as a high level of volatility (BAC: Daily returns) are the global crisis of 2007 and the COVID-19 health crisis.
# Plotting BMW Price
fig = plt.figure()
plt.figure(figsize=(20, 7))
plt.plot(df[["bmw.de"]], label = ('BMW historical prices'))
plt.legend(loc='upper right')
plt.title('BMW: historical prices', fontsize=16)
plt.show()
# Plotting BMW Returns
fig = plt.figure()
plt.figure(figsize=(20, 7))
plt.plot(df[["Ret_BMW"]], label = ('BMW historical prices'))
plt.legend(loc='upper right')
plt.title('BMW: Daily returns', fontsize=16)
plt.show()
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
# Plotting FORD Price
fig = plt.figure()
plt.figure(figsize=(20, 7))
plt.plot(df[["f"]], label = ('FORD historical prices'))
plt.legend(loc='upper right')
plt.title('FORD: historical prices', fontsize=16)
plt.show()
# Plotting FORD Returns
fig = plt.figure()
plt.figure(figsize=(20, 7))
plt.plot(df[["Ret_F"]], label = ('FORD historical prices'))
plt.legend(loc='upper right')
plt.title('FORD: Daily returns', fontsize=16)
plt.show()
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
Ford's prices dropped drastically from 2002-2003 until they reached their minimum in 2009 due to the global crisis in 2007. After this period, the prices started to increase and then decreased in 2011, which means that the company was affected by the 2011-2012 crisis that occurred in Germany. Thereafter, prices continued to decline. this analysis is confirmed by the high volatility of returns during these periods.
fig = plt.figure()
plt.figure(figsize=(20, 7))
plt.plot(df_RET[["Ret_BAC","Ret_DBK"]], label = ('BAC daily returns', 'DBK daily returns'))
plt.legend(loc='upper right')
plt.title('Daily Returns: Banking industry', fontsize=16)
plt.show()
<Figure size 432x288 with 0 Axes>
We can see that DBK was affected by the 2003 crisis, unlike LAC. However, the financial crisis of 2008 had a much greater impact on BAC than on DBK.
fig = plt.figure()
plt.figure(figsize=(20, 7))
plt.plot(df_RET[["Ret_BMW","Ret_F"]], label = ('BMW daily returns', 'FORD daily returns'))
plt.legend(loc='upper right')
plt.title('Daily Returns: Car manufacturers', fontsize=16)
plt.show()
<Figure size 432x288 with 0 Axes>
We can note that the level of volatility of Ford is higher than that of BMW. We can explain this by the fact that Ford was more affected by the crises that took place during the period studied.
# Specify ARCH model assumptions
ARCH_DBK = arch_model(DBK_RET, p = 1, q = 0, vol = 'ARCH', dist = 'normal')
# Fit the model
ARCH_result_DBK = ARCH_DBK.fit(update_freq=5, disp="off")
# Get model estimated volatility
ARCH_vol_DBK = ARCH_result_DBK.conditional_volatility
# Forecasting
Prediction_DBK = ARCH_result_DBK.forecast(horizon = 1, start = '2020-12-14')
Forecast_ARCH_DBK = Prediction_DBK
Forecast_ARCH_DBK
print(ARCH_result_DBK.summary())
Constant Mean - ARCH Model Results
==============================================================================
Dep. Variable: Ret_DBK R-squared: 0.000
Mean Model: Constant Mean Adj. R-squared: 0.000
Vol Model: ARCH Log-Likelihood: -11629.2
Distribution: Normal AIC: 23264.3
Method: Maximum Likelihood BIC: 23283.9
No. Observations: 5008
Date: Thu, Jun 02 2022 Df Residuals: 5007
Time: 23:44:07 Df Model: 1
Mean Model
==========================================================================
coef std err t P>|t| 95.0% Conf. Int.
--------------------------------------------------------------------------
mu -0.0319 3.729e-02 -0.856 0.392 [ -0.105,4.116e-02]
Volatility Model
========================================================================
coef std err t P>|t| 95.0% Conf. Int.
------------------------------------------------------------------------
omega 4.7845 0.265 18.042 9.083e-73 [ 4.265, 5.304]
alpha[1] 0.3039 4.624e-02 6.573 4.936e-11 [ 0.213, 0.395]
========================================================================
Covariance estimator: robust
# Calculate the RMSE
a = 90
DBK_90 = DBK_RET.iloc[-a:].index # We are going to use it for our plot (Plot 90 days of real observations)
b = 10 # 10 days of forecasting
DBK_10 = DBK_RET.iloc[-b:].index
rmse_arch = np.sqrt(mse(DBK_RET[-b:] / 100,
np.sqrt(Forecast_ARCH_DBK\
.variance.iloc[-len(DBK_10):]
/ 100)))
print('The RMSE value of ARCH model (DBK): {:.4f}'.format(rmse_arch))
The RMSE value of ARCH model (DBK): 0.2504
# Specify GARCH model assumptions
ARCH_BAC = arch_model(BAC_RET, p = 1, q = 0, vol = 'ARCH', dist = 'normal')
# Fit the model
ARCH_result_BAC = ARCH_BAC.fit(update_freq=5, disp="off")
# Get model estimated volatility
ARCH_vol_BAC = ARCH_result_BAC.conditional_volatility
# Forecasting
Prediction_BAC = ARCH_result_BAC.forecast(horizon = 1, start = '2020-12-14')
Forecast_ARCH_BAC = Prediction_BAC
Forecast_ARCH_BAC
print(ARCH_result_BAC.summary())
Constant Mean - ARCH Model Results
==============================================================================
Dep. Variable: Ret_BAC R-squared: 0.000
Mean Model: Constant Mean Adj. R-squared: 0.000
Vol Model: ARCH Log-Likelihood: -11212.0
Distribution: Normal AIC: 22429.9
Method: Maximum Likelihood BIC: 22449.5
No. Observations: 5008
Date: Thu, Jun 02 2022 Df Residuals: 5007
Time: 23:44:08 Df Model: 1
Mean Model
==============================================================================
coef std err t P>|t| 95.0% Conf. Int.
------------------------------------------------------------------------------
mu -5.3240e-04 4.322e-02 -1.232e-02 0.990 [-8.523e-02,8.417e-02]
Volatility Model
========================================================================
coef std err t P>|t| 95.0% Conf. Int.
------------------------------------------------------------------------
omega 2.6180 0.185 14.135 2.315e-45 [ 2.255, 2.981]
alpha[1] 1.0000 0.140 7.163 7.886e-13 [ 0.726, 1.274]
========================================================================
Covariance estimator: robust
# Calculate the RMSE
c = 90
BAC_90 = BAC_RET.iloc[-a:].index
d = 10
BAC_10 = BAC_RET.iloc[-b:].index
rmse_arch = np.sqrt(mse(BAC_RET[-d:] / 100,
np.sqrt(Forecast_ARCH_BAC\
.variance.iloc[-len(BAC_10):]
/ 100)))
print('The RMSE value of ARCH model (BAC): {:.4f}'.format(rmse_arch))
The RMSE value of ARCH model (BAC): 0.2273
# Ploting Returns/Conditional Variance
## DBK
figure(figsize=(20, 6), dpi=100)
plt.plot(ARCH_vol_DBK, color = 'red', label = 'Conditional Variance')
plt.plot(DBK_RET, color = 'blue',
label = 'Daily Returns', alpha = 0.4)
plt.legend(loc = 'upper right')
plt.title('ARCH: Returns/Conditional Variance (DBK)', fontsize=16)
plt.show()
## BAC
figure(figsize=(20, 6), dpi=100)
plt.plot(ARCH_vol_BAC, color = 'red', label = 'Conditional Variance')
plt.plot(BAC_RET, color = 'blue',
label = 'Daily Returns', alpha = 0.4)
plt.legend(loc = 'upper right')
plt.title('ARCH: Returns/Conditional Variance (BAC)', fontsize=16)
plt.show()
# Ploting Actual Returns/Predicted Returns
## DBK
plt.figure(figsize=(20, 7))
plt.plot(DBK_RET.iloc[-len(DBK_90):] / 100, label='Actual Returns')
plt.plot(Forecast_ARCH_DBK.variance.iloc[-len(DBK_10):] / 100,
label='Predicted Returns')
plt.title('ARCH: Actual Returns/Predicted Returns (DBK)', fontsize=16)
plt.legend()
plt.show()
## BAC
plt.figure(figsize=(20, 7))
plt.plot(BAC_RET.iloc[-len(BAC_90):] / 100, label='Actual Returns')
plt.plot(Forecast_ARCH_BAC.variance.iloc[-len(BAC_10):] / 100,
label='Predicted Returns')
plt.title('ARCH: Actual Returns/Predicted Returns (BAC)', fontsize=16)
plt.legend()
plt.show()
# Specify ARCH model assumptions
ARCH_BMW = arch_model(BMW_RET, p = 1, q = 0, vol = 'ARCH', dist = 'normal')
# Fit the model
ARCH_result_BMW = ARCH_BMW.fit(update_freq=5, disp="off")
# Get model estimated volatility
ARCH_vol_BMW = ARCH_result_BMW.conditional_volatility
# Forecasting
Prediction_BMW = ARCH_result_BMW.forecast(horizon = 1, start = '2020-12-14')
Forecast_ARCH_BMW = Prediction_BMW
Forecast_ARCH_BMW
print(ARCH_result_BMW.summary())
Constant Mean - ARCH Model Results
==============================================================================
Dep. Variable: Ret_BMW R-squared: 0.000
Mean Model: Constant Mean Adj. R-squared: 0.000
Vol Model: ARCH Log-Likelihood: -10687.0
Distribution: Normal AIC: 21380.0
Method: Maximum Likelihood BIC: 21399.5
No. Observations: 5008
Date: Thu, Jun 02 2022 Df Residuals: 5007
Time: 23:44:09 Df Model: 1
Mean Model
=============================================================================
coef std err t P>|t| 95.0% Conf. Int.
-----------------------------------------------------------------------------
mu 6.5426e-03 2.916e-02 0.224 0.822 [-5.060e-02,6.369e-02]
Volatility Model
========================================================================
coef std err t P>|t| 95.0% Conf. Int.
------------------------------------------------------------------------
omega 3.3410 0.170 19.670 3.863e-86 [ 3.008, 3.674]
alpha[1] 0.2752 4.378e-02 6.286 3.265e-10 [ 0.189, 0.361]
========================================================================
Covariance estimator: robust
# Calculate the RMSE
e = 90
BMW_90 = BMW_RET.iloc[-e:].index
f = 10
BMW_10 = BMW_RET.iloc[-f:].index
rmse_arch = np.sqrt(mse(BMW_RET[-f:] / 100,
np.sqrt(Forecast_ARCH_BMW\
.variance.iloc[-len(BMW_10):]
/ 100)))
print('The RMSE value of ARCH model (BMW): {:.4f}'.format(rmse_arch))
The RMSE value of ARCH model (BMW): 0.2005
# Specify ARCH model assumptions
ARCH_F = arch_model(F_RET, p = 1, q = 0, vol = 'ARCH', dist = 'normal')
# Fit the model
ARCH_result_F = ARCH_F.fit(update_freq=5, disp="off")
# Get model estimated volatility
ARCH_vol_F = ARCH_result_F.conditional_volatility
# Forecasting
Prediction_F = ARCH_result_F.forecast(horizon = 1, start = '2020-12-14')
Forecast_ARCH_F = Prediction_F
Forecast_ARCH_F
print(ARCH_result_F.summary())
Constant Mean - ARCH Model Results
==============================================================================
Dep. Variable: Ret_F R-squared: 0.000
Mean Model: Constant Mean Adj. R-squared: 0.000
Vol Model: ARCH Log-Likelihood: -11464.9
Distribution: Normal AIC: 22935.8
Method: Maximum Likelihood BIC: 22955.4
No. Observations: 5008
Date: Thu, Jun 02 2022 Df Residuals: 5007
Time: 23:44:09 Df Model: 1
Mean Model
=============================================================================
coef std err t P>|t| 95.0% Conf. Int.
-----------------------------------------------------------------------------
mu -0.0140 3.852e-02 -0.362 0.717 [-8.945e-02,6.155e-02]
Volatility Model
========================================================================
coef std err t P>|t| 95.0% Conf. Int.
------------------------------------------------------------------------
omega 3.7447 0.225 16.639 3.644e-62 [ 3.304, 4.186]
alpha[1] 0.5683 8.532e-02 6.660 2.730e-11 [ 0.401, 0.736]
========================================================================
Covariance estimator: robust
# Calculate the RMSE
g = 90
F_90 = F_RET.iloc[-g:].index
h = 10
F_10 = F_RET.iloc[-h:].index
rmse_arch = np.sqrt(mse(F_RET[-h:] / 100,
np.sqrt(Forecast_ARCH_F\
.variance.iloc[-len(F_10):]
/ 100)))
print('The RMSE value of ARCH model (Ford): {:.4f}'.format(rmse_arch))
The RMSE value of ARCH model (Ford): 0.2219
# Ploting Returns/Conditional Variance
## BMW
figure(figsize=(20, 6), dpi=100)
plt.plot(ARCH_vol_BMW, color = 'red', label = 'Conditional Variance')
plt.plot(BMW_RET, color = 'blue',
label = 'Daily Returns', alpha = 0.4)
plt.legend(loc = 'upper right')
plt.title('ARCH: Returns/Conditional Variance (BMW)', fontsize=16)
plt.show()
## Ford
figure(figsize=(20, 6), dpi=100)
plt.plot(ARCH_vol_F, color = 'red', label = 'Conditional Variance')
plt.plot(F_RET, color = 'blue',
label = 'Daily Returns', alpha = 0.4)
plt.legend(loc = 'upper right')
plt.title('ARCH: Returns/Conditional Variance (FORD)', fontsize=16)
plt.show()
# Ploting Actual Returns/Predicted Returns
## BMW
plt.figure(figsize=(20, 7))
plt.plot(BMW_RET.iloc[-len(BMW_90):] / 100, label='Actual Returns')
plt.plot(Forecast_ARCH_BMW.variance.iloc[-len(BMW_10):] / 100,
label='Predicted Returns')
plt.title('ARCH: Actual Returns/Predicted Returns (BMW)', fontsize=16)
plt.legend()
plt.show()
## FORD
plt.figure(figsize=(20, 7))
plt.plot(F_RET.iloc[-len(F_90):] / 100, label='Actual Returns')
plt.plot(Forecast_ARCH_F.variance.iloc[-len(F_10):] / 100,
label='Predicted Returns')
plt.title('ARCH: Actual Returns/Predicted Returns (FORD)', fontsize=16)
plt.legend()
plt.show()
# Specify GARCH model assumptions
GARCH_DBK = arch_model(DBK_RET, p = 1, q = 1, mean = 'constant', vol = 'GARCH', dist = 'normal')
# Fit the model
GARCH_result_DBK = GARCH_DBK.fit(update_freq=5, disp="off")
# Get model estimated volatility
GARCH_vol_DBK = GARCH_result_DBK.conditional_volatility
# Forecasting
Prediction_GARCH_DBK = GARCH_result_DBK.forecast(horizon = 1, start = '2020-12-14')
Forecast_GARCH_DBK = Prediction_GARCH_DBK
Forecast_GARCH_DBK
print(GARCH_result_DBK.summary())
Constant Mean - GARCH Model Results
==============================================================================
Dep. Variable: Ret_DBK R-squared: 0.000
Mean Model: Constant Mean Adj. R-squared: 0.000
Vol Model: GARCH Log-Likelihood: -10944.0
Distribution: Normal AIC: 21896.0
Method: Maximum Likelihood BIC: 21922.0
No. Observations: 5008
Date: Thu, Jun 02 2022 Df Residuals: 5007
Time: 23:44:10 Df Model: 1
Mean Model
==============================================================================
coef std err t P>|t| 95.0% Conf. Int.
------------------------------------------------------------------------------
mu -2.9844e-03 2.648e-02 -0.113 0.910 [-5.489e-02,4.892e-02]
Volatility Model
============================================================================
coef std err t P>|t| 95.0% Conf. Int.
----------------------------------------------------------------------------
omega 0.0361 1.302e-02 2.775 5.525e-03 [1.061e-02,6.163e-02]
alpha[1] 0.0687 1.056e-02 6.502 7.920e-11 [4.797e-02,8.937e-02]
beta[1] 0.9273 1.106e-02 83.826 0.000 [ 0.906, 0.949]
============================================================================
Covariance estimator: robust
# Calculate the RMSE
rmse_arch = np.sqrt(mse(DBK_RET[-b:] / 100,
np.sqrt(Forecast_GARCH_DBK\
.variance.iloc[-len(DBK_10):]
/ 100)))
print('The RMSE value of GARCH model (DBK): {:.4f}'.format(rmse_arch))
The RMSE value of GARCH model (DBK): 0.2076
# Specify GARCH model assumptions
GARCH_BAC = arch_model(BAC_RET, p = 1, q = 1, mean = 'constant', vol = 'GARCH', dist = 'normal')
# Fit the model
GARCH_result_BAC = GARCH_BAC.fit(update_freq=5, disp="off")
# Get model estimated volatility
GARCH_vol_BAC = GARCH_result_BAC.conditional_volatility
# Forecasting
Prediction_GARCH_BAC = GARCH_result_BAC.forecast(horizon = 1, start = '2020-12-14')
Forecast_GARCH_BAC = Prediction_GARCH_BAC
Forecast_GARCH_BAC
print(GARCH_result_BAC.summary())
Constant Mean - GARCH Model Results
==============================================================================
Dep. Variable: Ret_BAC R-squared: 0.000
Mean Model: Constant Mean Adj. R-squared: 0.000
Vol Model: GARCH Log-Likelihood: -10148.2
Distribution: Normal AIC: 20304.4
Method: Maximum Likelihood BIC: 20330.5
No. Observations: 5008
Date: Thu, Jun 02 2022 Df Residuals: 5007
Time: 23:44:11 Df Model: 1
Mean Model
=============================================================================
coef std err t P>|t| 95.0% Conf. Int.
-----------------------------------------------------------------------------
mu 0.0349 2.215e-02 1.577 0.115 [-8.477e-03,7.834e-02]
Volatility Model
============================================================================
coef std err t P>|t| 95.0% Conf. Int.
----------------------------------------------------------------------------
omega 0.0473 2.374e-02 1.993 4.627e-02 [7.834e-04,9.383e-02]
alpha[1] 0.0941 2.359e-02 3.989 6.649e-05 [4.786e-02, 0.140]
beta[1] 0.8983 2.488e-02 36.103 2.008e-285 [ 0.849, 0.947]
============================================================================
Covariance estimator: robust
# Calculate the RMSE
rmse_arch = np.sqrt(mse(BAC_RET[-d:] / 100,
np.sqrt(Forecast_GARCH_BAC\
.variance.iloc[-len(BAC_10):]
/ 100)))
print('The RMSE value of GARCH model (BAC): {:.4f}'.format(rmse_arch))
The RMSE value of GARCH model (BAC): 0.1982
# Ploting Returns/Conditional Variance
## DBK
figure(figsize=(20, 6), dpi=100)
plt.plot(GARCH_vol_DBK, color = 'red', label = 'Conditional Variance')
plt.plot(DBK_RET, color = 'blue',
label = 'Daily Returns', alpha = 0.4)
plt.legend(loc = 'upper right')
plt.title('GARCH: Returns/Conditional Variance (DBK)', fontsize=16)
plt.show()
## BAC
figure(figsize=(20, 6), dpi=100)
plt.plot(GARCH_vol_BAC, color = 'red', label = 'Conditional Variance')
plt.plot(BAC_RET, color = 'blue',
label = 'Daily Returns', alpha = 0.4)
plt.legend(loc = 'upper right')
plt.title('GARCH: Returns/Conditional Variance (BAC)', fontsize=16)
plt.show()
# Ploting Actual Returns/Predicted Returns
## DBK
plt.figure(figsize=(20, 6))
plt.plot(DBK_RET.iloc[-len(DBK_90):] / 100, label='Actual Returns')
plt.plot(Forecast_GARCH_DBK.variance.iloc[-len(DBK_10):] / 100,
label='Predicted Returns')
plt.title('GARCH: Actual Returns/Predicted Returns (DBK)', fontsize=16)
plt.legend()
plt.show()
## BAC
plt.figure(figsize=(20, 6))
plt.plot(BAC_RET.iloc[-len(BAC_90):] / 100, label='Actual Returns')
plt.plot(Forecast_GARCH_BAC.variance.iloc[-len(BAC_10):] / 100,
label='Predicted Returns')
plt.title('GARCH: Actual Returns/Predicted Returns (BAC)', fontsize=16)
plt.legend()
plt.show()
# Specify GARCH model assumptions
GARCH_BMW = arch_model(BMW_RET, p = 1, q = 1, mean = 'constant', vol = 'GARCH', dist = 'normal')
# Fit the model
GARCH_result_BMW = GARCH_BMW.fit(update_freq=5, disp="off")
# Get model estimated volatility
GARCH_vol_BMW = GARCH_result_BMW.conditional_volatility
# Forecasting
Prediction_GARCH_BMW = GARCH_result_BMW.forecast(horizon = 1, start = '2020-12-14')
Forecast_GARCH_BMW = Prediction_GARCH_BMW
Forecast_GARCH_BMW
print(GARCH_result_BMW.summary())
Constant Mean - GARCH Model Results
==============================================================================
Dep. Variable: Ret_BMW R-squared: 0.000
Mean Model: Constant Mean Adj. R-squared: 0.000
Vol Model: GARCH Log-Likelihood: -10114.2
Distribution: Normal AIC: 20236.4
Method: Maximum Likelihood BIC: 20262.4
No. Observations: 5008
Date: Thu, Jun 02 2022 Df Residuals: 5007
Time: 23:44:12 Df Model: 1
Mean Model
=============================================================================
coef std err t P>|t| 95.0% Conf. Int.
-----------------------------------------------------------------------------
mu 0.0345 2.325e-02 1.485 0.138 [-1.105e-02,8.007e-02]
Volatility Model
============================================================================
coef std err t P>|t| 95.0% Conf. Int.
----------------------------------------------------------------------------
omega 0.0314 1.083e-02 2.898 3.755e-03 [1.016e-02,5.263e-02]
alpha[1] 0.0609 1.005e-02 6.054 1.415e-09 [4.116e-02,8.057e-02]
beta[1] 0.9324 1.101e-02 84.709 0.000 [ 0.911, 0.954]
============================================================================
Covariance estimator: robust
# Calculate the RMSE
rmse_arch = np.sqrt(mse(BMW_RET[-f:] / 100,
np.sqrt(Forecast_GARCH_BMW\
.variance.iloc[-len(BMW_10):]
/ 100)))
print('The RMSE value of GARCH model (BMW): {:.4f}'.format(rmse_arch))
The RMSE value of GARCH model (BMW): 0.1828
# Specify GARCH model assumptions
GARCH_F = arch_model(F_RET, p = 1, q = 1, mean = 'constant', vol = 'GARCH', dist = 'normal')
# Fit the model
GARCH_result_F = GARCH_F.fit(update_freq=5, disp="off")
# Get model estimated volatility
GARCH_vol_F = GARCH_result_F.conditional_volatility
# Forecasting
Prediction_GARCH_F = GARCH_result_F.forecast(horizon = 1, start = '2020-12-14')
Forecast_GARCH_F = Prediction_GARCH_F
Forecast_GARCH_F
print(GARCH_result_F.summary())
Constant Mean - GARCH Model Results
==============================================================================
Dep. Variable: Ret_F R-squared: 0.000
Mean Model: Constant Mean Adj. R-squared: 0.000
Vol Model: GARCH Log-Likelihood: -10945.9
Distribution: Normal AIC: 21899.8
Method: Maximum Likelihood BIC: 21925.9
No. Observations: 5008
Date: Thu, Jun 02 2022 Df Residuals: 5007
Time: 23:44:12 Df Model: 1
Mean Model
==========================================================================
coef std err t P>|t| 95.0% Conf. Int.
--------------------------------------------------------------------------
mu -0.0453 2.828e-02 -1.602 0.109 [ -0.101,1.013e-02]
Volatility Model
===========================================================================
coef std err t P>|t| 95.0% Conf. Int.
---------------------------------------------------------------------------
omega 0.0704 3.651e-02 1.929 5.377e-02 [-1.143e-03, 0.142]
alpha[1] 0.0664 2.037e-02 3.261 1.110e-03 [2.650e-02, 0.106]
beta[1] 0.9221 2.491e-02 37.013 7.028e-300 [ 0.873, 0.971]
===========================================================================
Covariance estimator: robust
# Calculate the RMSE
rmse_arch = np.sqrt(mse(F_RET[-h:] / 100,
np.sqrt(Forecast_GARCH_F\
.variance.iloc[-len(F_10):]
/ 100)))
print('The RMSE value of GARCH model (Ford): {:.4f}'.format(rmse_arch))
The RMSE value of GARCH model (Ford): 0.2174
# Ploting Returns/Conditional Variance
## BMW
figure(figsize=(20, 6), dpi=100)
plt.plot(GARCH_vol_BMW, color = 'red', label = 'Conditional Variance')
plt.plot(BMW_RET, color = 'blue',
label = 'Daily Returns', alpha = 0.4)
plt.legend(loc = 'upper right')
plt.title('GARCH: Returns/Conditional Variance (BMW)', fontsize=16)
plt.show()
## Ford
figure(figsize=(20, 6), dpi=100)
plt.plot(GARCH_vol_F, color = 'red', label = 'Conditional Variance')
plt.plot(F_RET, color = 'blue',
label = 'Daily Returns', alpha = 0.4)
plt.legend(loc = 'upper right')
plt.title('GARCH: Returns/Conditional Variance (FORD)', fontsize=16)
plt.show()
# Ploting Actual Returns/Predicted Returns
## BMW
plt.figure(figsize=(20, 6))
plt.plot(BMW_RET.iloc[-len(BMW_90):] / 100, label='Actual Returns')
plt.plot(Forecast_GARCH_BMW.variance.iloc[-len(BMW_10):] / 100,
label='Predicted Returns')
plt.title('GARCH: Actual Returns/Predicted Returns (BMW)', fontsize=16)
plt.legend()
plt.show()
## Ford
plt.figure(figsize=(20, 6))
plt.plot(F_RET.iloc[-len(F_90):] / 100, label='Actual Returns')
plt.plot(Forecast_GARCH_F.variance.iloc[-len(F_10):] / 100,
label='Predicted Returns')
plt.title('GARCH: Actual Returns/Predicted Returns (FORD)', fontsize=16)
plt.legend()
plt.show()
# Specify GARCH model assumptions
GARCHt_DBK = arch_model(DBK_RET, p = 1,o=0, q = 1, vol = 'GARCH', dist = "StudentsT")
# Fit the model
GARCHt_result_DBK = GARCHt_DBK.fit(update_freq=5, disp="off")
# Get model estimated volatility
GARCHt_vol_DBK = GARCHt_result_DBK.conditional_volatility
# Forecasting
Prediction_GARCHt_DBK = GARCHt_result_DBK.forecast(horizon = 1, start = '2020-12-14')
Forecast_GARCHt_DBK = Prediction_GARCHt_DBK
Forecast_GARCHt_DBK
print(GARCHt_result_DBK.summary())
Constant Mean - GARCH Model Results
====================================================================================
Dep. Variable: Ret_DBK R-squared: 0.000
Mean Model: Constant Mean Adj. R-squared: 0.000
Vol Model: GARCH Log-Likelihood: -10842.2
Distribution: Standardized Student's t AIC: 21694.3
Method: Maximum Likelihood BIC: 21726.9
No. Observations: 5008
Date: Thu, Jun 02 2022 Df Residuals: 5007
Time: 23:44:14 Df Model: 1
Mean Model
==============================================================================
coef std err t P>|t| 95.0% Conf. Int.
------------------------------------------------------------------------------
mu -3.4032e-03 2.504e-02 -0.136 0.892 [-5.247e-02,4.567e-02]
Volatility Model
============================================================================
coef std err t P>|t| 95.0% Conf. Int.
----------------------------------------------------------------------------
omega 0.0389 1.206e-02 3.222 1.275e-03 [1.522e-02,6.250e-02]
alpha[1] 0.0676 8.938e-03 7.563 3.953e-14 [5.008e-02,8.511e-02]
beta[1] 0.9276 9.268e-03 100.084 0.000 [ 0.909, 0.946]
Distribution
========================================================================
coef std err t P>|t| 95.0% Conf. Int.
------------------------------------------------------------------------
nu 7.0362 0.667 10.543 5.460e-26 [ 5.728, 8.344]
========================================================================
Covariance estimator: robust
# Calculate the RMSE
rmse_arch = np.sqrt(mse(DBK_RET[-b:] / 100,
np.sqrt(Forecast_GARCHt_DBK\
.variance.iloc[-len(DBK_10):]
/ 100)))
print('The RMSE value of GARCH-t model (DBK): {:.4f}'.format(rmse_arch))
The RMSE value of GARCH-t model (DBK): 0.2074
# Specify GARCH model assumptions
GARCHt_BAC = arch_model(BAC_RET, p = 1,o=0, q = 1, vol = 'GARCH', dist = "StudentsT")
# Fit the model
GARCHt_result_BAC = GARCHt_BAC.fit(update_freq=5, disp="off")
# Get model estimated volatility
GARCHt_vol_BAC = GARCHt_result_BAC.conditional_volatility
# Forecasting
Prediction_GARCHt_BAC = GARCHt_result_BAC.forecast(horizon = 1, start = '2020-12-14')
Forecast_GARCHt_BAC = Prediction_GARCHt_BAC
Forecast_GARCHt_BAC
print(GARCHt_result_BAC.summary())
Constant Mean - GARCH Model Results
====================================================================================
Dep. Variable: Ret_BAC R-squared: 0.000
Mean Model: Constant Mean Adj. R-squared: 0.000
Vol Model: GARCH Log-Likelihood: -9923.29
Distribution: Standardized Student's t AIC: 19856.6
Method: Maximum Likelihood BIC: 19889.2
No. Observations: 5008
Date: Thu, Jun 02 2022 Df Residuals: 5007
Time: 23:44:14 Df Model: 1
Mean Model
============================================================================
coef std err t P>|t| 95.0% Conf. Int.
----------------------------------------------------------------------------
mu 0.0418 1.793e-02 2.334 1.961e-02 [6.701e-03,7.698e-02]
Volatility Model
============================================================================
coef std err t P>|t| 95.0% Conf. Int.
----------------------------------------------------------------------------
omega 0.0280 9.141e-03 3.061 2.205e-03 [1.007e-02,4.590e-02]
alpha[1] 0.1001 1.548e-02 6.467 9.995e-11 [6.979e-02, 0.130]
beta[1] 0.8999 1.503e-02 59.879 0.000 [ 0.870, 0.929]
Distribution
========================================================================
coef std err t P>|t| 95.0% Conf. Int.
------------------------------------------------------------------------
nu 5.3811 0.409 13.142 1.885e-39 [ 4.579, 6.184]
========================================================================
Covariance estimator: robust
# Calculate the RMSE
rmse_arch = np.sqrt(mse(BAC_RET[-d:] / 100,
np.sqrt(Forecast_GARCHt_BAC\
.variance.iloc[-len(BAC_10):]
/ 100)))
print('The RMSE value of GARCH-t model (BAC): {:.4f}'.format(rmse_arch))
The RMSE value of GARCH-t model (BAC): 0.2018
# Ploting Returns/Conditional Variance
## DBK
figure(figsize=(20, 6), dpi=100)
plt.plot(GARCHt_vol_DBK, color = 'red', label = 'Conditional Variance')
plt.plot(DBK_RET, color = 'blue',
label = 'Daily Returns', alpha = 0.4)
plt.legend(loc = 'upper right')
plt.title('GARCH-t: Returns/Conditional Variance (DBK)', fontsize=16)
plt.show()
## BAC
figure(figsize=(20, 6), dpi=100)
plt.plot(GARCHt_vol_BAC, color = 'red', label = 'Conditional Variance')
plt.plot(BAC_RET, color = 'blue',
label = 'Daily Returns', alpha = 0.4)
plt.legend(loc = 'upper right')
plt.title('GARCH-t: Returns/Conditional Variance (BAC)', fontsize=16)
plt.show()
# Ploting Actual Returns/Predicted Returns
## DBK
plt.figure(figsize=(20, 6))
plt.plot(DBK_RET.iloc[-len(DBK_90):] / 100, label='Actual Returns')
plt.plot(Forecast_GARCHt_DBK.variance.iloc[-len(DBK_10):] / 100,
label='Predicted Returns')
plt.title('GARCH-t: Actual Returns/Predicted Returns (DBK)', fontsize=16)
plt.legend()
plt.show()
## BAC
plt.figure(figsize=(20, 6))
plt.plot(BAC_RET.iloc[-len(BAC_90):] / 100, label='Actual Returns')
plt.plot(Forecast_GARCHt_BAC.variance.iloc[-len(BAC_10):] / 100,
label='Predicted Returns')
plt.title('GARCH-t: Actual Returns/Predicted Returns (BAC)', fontsize=16)
plt.legend()
plt.show()
# Specify GARCH model assumptions
GARCHt_BMW = arch_model(BMW_RET, p = 1,o=0, q = 1, vol = 'GARCH', dist = "StudentsT")
# Fit the model
GARCHt_result_BMW = GARCHt_BMW.fit(update_freq=5, disp="off")
# Get model estimated volatility
GARCHt_vol_BMW = GARCHt_result_BMW.conditional_volatility
# Forecasting
Prediction_GARCHt_BMW = GARCHt_result_BMW.forecast(horizon = 1, start = '2020-12-14')
Forecast_GARCHt_BMW = Prediction_GARCHt_BMW
Forecast_GARCHt_BMW
print(GARCHt_result_BMW.summary())
Constant Mean - GARCH Model Results
====================================================================================
Dep. Variable: Ret_BMW R-squared: 0.000
Mean Model: Constant Mean Adj. R-squared: 0.000
Vol Model: GARCH Log-Likelihood: -9979.97
Distribution: Standardized Student's t AIC: 19969.9
Method: Maximum Likelihood BIC: 20002.5
No. Observations: 5008
Date: Thu, Jun 02 2022 Df Residuals: 5007
Time: 23:44:15 Df Model: 1
Mean Model
=============================================================================
coef std err t P>|t| 95.0% Conf. Int.
-----------------------------------------------------------------------------
mu 0.0238 2.100e-02 1.135 0.256 [-1.732e-02,6.498e-02]
Volatility Model
============================================================================
coef std err t P>|t| 95.0% Conf. Int.
----------------------------------------------------------------------------
omega 0.0219 7.483e-03 2.927 3.424e-03 [7.235e-03,3.657e-02]
alpha[1] 0.0555 8.646e-03 6.423 1.336e-10 [3.859e-02,7.248e-02]
beta[1] 0.9408 8.933e-03 105.322 0.000 [ 0.923, 0.958]
Distribution
========================================================================
coef std err t P>|t| 95.0% Conf. Int.
------------------------------------------------------------------------
nu 5.9814 0.480 12.465 1.156e-35 [ 5.041, 6.922]
========================================================================
Covariance estimator: robust
# Calculate the RMSE
rmse_arch = np.sqrt(mse(BMW_RET[-f:] / 100,
np.sqrt(Forecast_GARCHt_BMW\
.variance.iloc[-len(BMW_10):]
/ 100)))
print('The RMSE value of GARCH-t model (BMW): {:.4f}'.format(rmse_arch))
The RMSE value of GARCH-t model (BMW): 0.1860
# Specify GARCH model assumptions
GARCHt_F = arch_model(F_RET, p = 1,o=0, q = 1, vol = 'GARCH', dist = "StudentsT")
# Fit the model
GARCHt_result_F = GARCHt_F.fit(update_freq=5, disp="off")
# Get model estimated volatility
GARCHt_vol_F = GARCHt_result_F.conditional_volatility
# Forecasting
Prediction_GARCHt_F = GARCHt_result_F.forecast(horizon = 1, start = '2020-12-14')
Forecast_GARCHt_F = Prediction_GARCHt_F
Forecast_GARCHt_F
print(GARCHt_result_F.summary())
Constant Mean - GARCH Model Results
====================================================================================
Dep. Variable: Ret_F R-squared: 0.000
Mean Model: Constant Mean Adj. R-squared: 0.000
Vol Model: GARCH Log-Likelihood: -10707.5
Distribution: Standardized Student's t AIC: 21424.9
Method: Maximum Likelihood BIC: 21457.5
No. Observations: 5008
Date: Thu, Jun 02 2022 Df Residuals: 5007
Time: 23:44:16 Df Model: 1
Mean Model
=============================================================================
coef std err t P>|t| 95.0% Conf. Int.
-----------------------------------------------------------------------------
mu -0.0221 2.353e-02 -0.939 0.348 [-6.821e-02,2.403e-02]
Volatility Model
============================================================================
coef std err t P>|t| 95.0% Conf. Int.
----------------------------------------------------------------------------
omega 0.0334 1.599e-02 2.087 3.688e-02 [2.032e-03,6.470e-02]
alpha[1] 0.0619 1.500e-02 4.129 3.644e-05 [3.254e-02,9.135e-02]
beta[1] 0.9350 1.594e-02 58.645 0.000 [ 0.904, 0.966]
Distribution
========================================================================
coef std err t P>|t| 95.0% Conf. Int.
------------------------------------------------------------------------
nu 5.0964 0.368 13.855 1.185e-43 [ 4.375, 5.817]
========================================================================
Covariance estimator: robust
# Calculate the RMSE
rmse_arch = np.sqrt(mse(F_RET[-h:] / 100,
np.sqrt(Forecast_GARCHt_F\
.variance.iloc[-len(F_10):]
/ 100)))
print('The RMSE value of GARCH-t model (Ford): {:.4f}'.format(rmse_arch))
The RMSE value of GARCH-t model (Ford): 0.2236
# Ploting Returns/Conditional Variance
## BMW
figure(figsize=(20, 6), dpi=100)
plt.plot(GARCHt_vol_BMW, color = 'red', label = 'Conditional Variance')
plt.plot(BMW_RET, color = 'blue',
label = 'Daily Returns', alpha = 0.4)
plt.legend(loc = 'upper right')
plt.title('GARCH-t: Returns/Conditional Variance (BMW)', fontsize=16)
plt.show()
## FORD
figure(figsize=(20, 6), dpi=100)
plt.plot(GARCHt_vol_F, color = 'red', label = 'Conditional Variance')
plt.plot(F_RET, color = 'blue',
label = 'Daily Returns', alpha = 0.4)
plt.legend(loc = 'upper right')
plt.title('GARCH-t: Returns/Conditional Variance (FORD)', fontsize=16)
plt.show()
# Ploting Actual Returns/Predicted Returns
## BMW
plt.figure(figsize=(20, 6))
plt.plot(BMW_RET.iloc[-len(BMW_90):] / 100, label='Actual Returns')
plt.plot(Forecast_GARCHt_BMW.variance.iloc[-len(BMW_10):] / 100,
label='Predicted Returns')
plt.title('GARCH-t: Actual Returns/Predicted Returns (BMW)', fontsize=16)
plt.legend()
plt.show()
## FORD
plt.figure(figsize=(20, 6))
plt.plot(F_RET.iloc[-len(F_90):] / 100, label='Actual Returns')
plt.plot(Forecast_GARCHt_F.variance.iloc[-len(F_10):] / 100,
label='Predicted Returns')
plt.title('GARCH-t: Actual Returns/Predicted Returns (FORD)', fontsize=16)
plt.legend()
plt.show()
# Specify GARCH model assumptions
EGARCH_DBK = arch_model(DBK_RET, p = 1, q = 1, vol = 'EGARCH', dist = 'normal')
# Fit the model
EGARCH_result_DBK = EGARCH_DBK.fit(update_freq=5, disp="off")
# Get model estimated volatility
EGARCH_vol_DBK = EGARCH_result_DBK.conditional_volatility
# Forecasting
Prediction_EGARCH_DBK = EGARCH_result_DBK.forecast(horizon = 1, start = '2020-12-14')
Forecast_EGARCH_DBK = Prediction_EGARCH_DBK
Forecast_EGARCH_DBK
print(EGARCH_result_DBK.summary())
Constant Mean - EGARCH Model Results
==============================================================================
Dep. Variable: Ret_DBK R-squared: 0.000
Mean Model: Constant Mean Adj. R-squared: 0.000
Vol Model: EGARCH Log-Likelihood: -10941.9
Distribution: Normal AIC: 21891.7
Method: Maximum Likelihood BIC: 21917.8
No. Observations: 5008
Date: Thu, Jun 02 2022 Df Residuals: 5007
Time: 23:44:17 Df Model: 1
Mean Model
=============================================================================
coef std err t P>|t| 95.0% Conf. Int.
-----------------------------------------------------------------------------
mu 4.5026e-04 2.620e-02 1.718e-02 0.986 [-5.091e-02,5.181e-02]
Volatility Model
============================================================================
coef std err t P>|t| 95.0% Conf. Int.
----------------------------------------------------------------------------
omega 0.0198 4.982e-03 3.973 7.099e-05 [1.003e-02,2.956e-02]
alpha[1] 0.1554 1.832e-02 8.482 2.212e-17 [ 0.120, 0.191]
beta[1] 0.9911 2.730e-03 363.083 0.000 [ 0.986, 0.996]
============================================================================
Covariance estimator: robust
# Calculate the RMSE
rmse_arch = np.sqrt(mse(DBK_RET[-b:] / 100,
np.sqrt(Forecast_EGARCH_DBK\
.variance.iloc[-len(DBK_10):]
/ 100)))
print('The RMSE value of EGARCH model (DBK): {:.4f}'.format(rmse_arch))
The RMSE value of EGARCH model (DBK): 0.2161
# Specify GARCH model assumptions
EGARCH_BAC = arch_model(BAC_RET, p = 1, q = 1, vol = 'EGARCH', dist = 'normal')
# Fit the model
EGARCH_result_BAC = EGARCH_BAC.fit(update_freq=5, disp="off")
# Get model estimated volatility
EGARCH_vol_BAC = EGARCH_result_BAC.conditional_volatility
# Forecasting
Prediction_EGARCH_BAC = EGARCH_result_BAC.forecast(horizon = 1, start = '2020-12-14')
Forecast_EGARCH_BAC = Prediction_EGARCH_BAC
Forecast_EGARCH_BAC
print(EGARCH_result_BAC.summary())
Constant Mean - EGARCH Model Results
==============================================================================
Dep. Variable: Ret_BAC R-squared: 0.000
Mean Model: Constant Mean Adj. R-squared: 0.000
Vol Model: EGARCH Log-Likelihood: -10161.4
Distribution: Normal AIC: 20330.7
Method: Maximum Likelihood BIC: 20356.8
No. Observations: 5008
Date: Thu, Jun 02 2022 Df Residuals: 5007
Time: 23:44:17 Df Model: 1
Mean Model
============================================================================
coef std err t P>|t| 95.0% Conf. Int.
----------------------------------------------------------------------------
mu 0.0442 1.355e-03 32.660 5.707e-234 [4.159e-02,4.689e-02]
Volatility Model
============================================================================
coef std err t P>|t| 95.0% Conf. Int.
----------------------------------------------------------------------------
omega 0.0232 5.739e-03 4.039 5.377e-05 [1.193e-02,3.443e-02]
alpha[1] 0.1705 3.400e-02 5.013 5.360e-07 [ 0.104, 0.237]
beta[1] 0.9894 3.086e-03 320.639 0.000 [ 0.983, 0.995]
============================================================================
Covariance estimator: robust
# Calculate the RMSE
rmse_arch = np.sqrt(mse(BAC_RET[-d:] / 100,
np.sqrt(Forecast_EGARCH_BAC\
.variance.iloc[-len(BAC_10):]
/ 100)))
print('The RMSE value of EGARCH model (BAC): {:.4f}'.format(rmse_arch))
The RMSE value of EGARCH model (BAC): 0.1997
# Ploting Returns/Conditional Variance
## DBK
figure(figsize=(20, 6), dpi=100)
plt.plot(EGARCH_vol_DBK, color = 'red', label = 'Conditional Variance')
plt.plot(DBK_RET, color = 'blue',
label = 'Daily Returns', alpha = 0.4)
plt.legend(loc = 'upper right')
plt.title('EGARCH: Returns/Conditional Variance (DBK)', fontsize=16)
plt.show()
## BAC
figure(figsize=(20, 6), dpi=100)
plt.plot(EGARCH_vol_BAC, color = 'red', label = 'Conditional Variance')
plt.plot(BAC_RET, color = 'blue',
label = 'Daily Returns', alpha = 0.4)
plt.legend(loc = 'upper right')
plt.title('EGARCH: Returns/Conditional Variance (BAC)', fontsize=16)
plt.show()
# Ploting Actual Returns/Predicted Returns
## DBK
plt.figure(figsize=(20, 6))
plt.plot(DBK_RET.iloc[-len(DBK_90):] / 100, label='Actual Returns')
plt.plot(Forecast_EGARCH_DBK.variance.iloc[-len(DBK_10):] / 100,
label='Predicted Returns')
plt.title('EGARCH: Actual Returns/Predicted Returns (DBK)', fontsize=16)
plt.legend()
plt.show()
## BAC
plt.figure(figsize=(20, 6))
plt.plot(BAC_RET.iloc[-len(BAC_90):] / 100, label='Actual Returns')
plt.plot(Forecast_EGARCH_BAC.variance.iloc[-len(BAC_10):] / 100,
label='Predicted Returns')
plt.title('EGARCH: Actual Returns/Predicted Returns (BAC)', fontsize=16)
plt.legend()
plt.show()
# Specify GARCH model assumptions
EGARCH_BMW = arch_model(BMW_RET, p = 1, q = 1, vol = 'EGARCH', dist = 'normal')
# Fit the model
EGARCH_result_BMW = EGARCH_BMW.fit(update_freq=5, disp="off")
# Get model estimated volatility
EGARCH_vol_BMW = EGARCH_result_BMW.conditional_volatility
# Forecasting
Prediction_EGARCH_BMW = EGARCH_result_BMW.forecast(horizon = 1, start = '2020-12-14')
Forecast_EGARCH_BMW = Prediction_EGARCH_BMW
Forecast_EGARCH_BMW
print(EGARCH_result_BMW.summary())
Constant Mean - EGARCH Model Results
==============================================================================
Dep. Variable: Ret_BMW R-squared: 0.000
Mean Model: Constant Mean Adj. R-squared: 0.000
Vol Model: EGARCH Log-Likelihood: -10114.9
Distribution: Normal AIC: 20237.7
Method: Maximum Likelihood BIC: 20263.8
No. Observations: 5008
Date: Thu, Jun 02 2022 Df Residuals: 5007
Time: 23:44:19 Df Model: 1
Mean Model
=============================================================================
coef std err t P>|t| 95.0% Conf. Int.
-----------------------------------------------------------------------------
mu 0.0333 2.317e-02 1.437 0.151 [-1.212e-02,7.868e-02]
Volatility Model
============================================================================
coef std err t P>|t| 95.0% Conf. Int.
----------------------------------------------------------------------------
omega 0.0200 5.101e-03 3.927 8.605e-05 [1.003e-02,3.003e-02]
alpha[1] 0.1461 1.954e-02 7.479 7.471e-14 [ 0.108, 0.184]
beta[1] 0.9890 3.205e-03 308.635 0.000 [ 0.983, 0.995]
============================================================================
Covariance estimator: robust
# Calculate the RMSE
rmse_arch = np.sqrt(mse(BMW_RET[-f:] / 100,
np.sqrt(Forecast_EGARCH_BMW\
.variance.iloc[-len(BMW_10):]
/ 100)))
print('The RMSE value of EGARCH model (BMW): {:.4f}'.format(rmse_arch))
The RMSE value of EGARCH model (BMW): 0.1836
# Specify GARCH model assumptions
EGARCH_F = arch_model(F_RET, p = 1, q = 1, vol = 'EGARCH', dist = 'normal')
# Fit the model
EGARCH_result_F = EGARCH_F.fit(update_freq=5, disp="off")
# Get model estimated volatility
EGARCH_vol_F = EGARCH_result_F.conditional_volatility
# Forecasting
Prediction_EGARCH_F = EGARCH_result_F.forecast(horizon = 1, start = '2020-12-14')
Forecast_EGARCH_F = Prediction_EGARCH_F
Forecast_EGARCH_F
print(EGARCH_result_F.summary())
Constant Mean - EGARCH Model Results
==============================================================================
Dep. Variable: Ret_F R-squared: 0.000
Mean Model: Constant Mean Adj. R-squared: 0.000
Vol Model: EGARCH Log-Likelihood: -10931.5
Distribution: Normal AIC: 21871.0
Method: Maximum Likelihood BIC: 21897.1
No. Observations: 5008
Date: Thu, Jun 02 2022 Df Residuals: 5007
Time: 23:44:19 Df Model: 1
Mean Model
=============================================================================
coef std err t P>|t| 95.0% Conf. Int.
-----------------------------------------------------------------------------
mu -0.0398 2.834e-02 -1.405 0.160 [-9.536e-02,1.574e-02]
Volatility Model
============================================================================
coef std err t P>|t| 95.0% Conf. Int.
----------------------------------------------------------------------------
omega 0.0328 1.434e-02 2.285 2.228e-02 [4.669e-03,6.089e-02]
alpha[1] 0.1614 3.715e-02 4.346 1.386e-05 [8.864e-02, 0.234]
beta[1] 0.9850 7.668e-03 128.447 0.000 [ 0.970, 1.000]
============================================================================
Covariance estimator: robust
# Calculate the RMSE
rmse_arch = np.sqrt(mse(F_RET[-h:] / 100,
np.sqrt(Forecast_EGARCH_F\
.variance.iloc[-len(F_10):]
/ 100)))
print('The RMSE value of EGARCH model (FORD): {:.4f}'.format(rmse_arch))
The RMSE value of EGARCH model (FORD): 0.2243
# Ploting Returns/Conditional Variance
## BMW
figure(figsize=(20, 6), dpi=100)
plt.plot(EGARCH_vol_BMW, color = 'red', label = 'Conditional Variance')
plt.plot(BMW_RET, color = 'blue',
label = 'Daily Returns', alpha = 0.4)
plt.legend(loc = 'upper right')
plt.title('EGARCH: Returns/Conditional Variance (BMW)', fontsize=16)
plt.show()
## FORD
figure(figsize=(20, 6), dpi=100)
plt.plot(EGARCH_vol_F, color = 'red', label = 'Conditional Variance')
plt.plot(F_RET, color = 'blue',
label = 'Daily Returns', alpha = 0.4)
plt.legend(loc = 'upper right')
plt.title('EGARCH: Returns/Conditional Variance (FORD)', fontsize=16)
plt.show()
# Ploting Actual Returns/Predicted Returns
## BMW
plt.figure(figsize=(20, 6))
plt.plot(BMW_RET.iloc[-len(BMW_90):] / 100, label='Actual Returns')
plt.plot(Forecast_EGARCH_BMW.variance.iloc[-len(BMW_10):] / 100,
label='Predicted Returns')
plt.title('EGARCH: Actual Returns/Predicted Returns (BMW)', fontsize=16)
plt.legend()
plt.show()
## FORD
plt.figure(figsize=(20, 6))
plt.plot(F_RET.iloc[-len(F_90):] / 100, label='Actual Returns')
plt.plot(Forecast_EGARCH_F.variance.iloc[-len(F_10):] / 100,
label='Predicted Returns')
plt.title('EGARCH: Actual Returns/Predicted Returns (FORD)', fontsize=16)
plt.legend()
plt.show()
# Specify GARCH model assumptions
GJR_GARCH_DBK = arch_model(DBK_RET, p=1, o=1, q=1, dist = 'normal')
# Fit the model
GJR_GARCH_result_DBK = GJR_GARCH_DBK.fit(update_freq=5, disp="off")
# Get model estimated volatility
GJR_GARCH_vol_DBK = GJR_GARCH_result_DBK.conditional_volatility
# Forecasting
Prediction_GJR_GARCH_DBK = GJR_GARCH_result_DBK.forecast(horizon = 1, start = '2020-12-14')
Forecast_GJR_GARCH_DBK = Prediction_GJR_GARCH_DBK
Forecast_GJR_GARCH_DBK
print(GJR_GARCH_result_DBK.summary())
Constant Mean - GJR-GARCH Model Results
==============================================================================
Dep. Variable: Ret_DBK R-squared: 0.000
Mean Model: Constant Mean Adj. R-squared: 0.000
Vol Model: GJR-GARCH Log-Likelihood: -10921.4
Distribution: Normal AIC: 21852.8
Method: Maximum Likelihood BIC: 21885.4
No. Observations: 5008
Date: Thu, Jun 02 2022 Df Residuals: 5007
Time: 23:44:20 Df Model: 1
Mean Model
=============================================================================
coef std err t P>|t| 95.0% Conf. Int.
-----------------------------------------------------------------------------
mu -0.0359 2.608e-02 -1.375 0.169 [-8.697e-02,1.527e-02]
Volatility Model
============================================================================
coef std err t P>|t| 95.0% Conf. Int.
----------------------------------------------------------------------------
omega 0.0332 1.248e-02 2.662 7.763e-03 [8.763e-03,5.768e-02]
alpha[1] 0.0307 1.123e-02 2.736 6.215e-03 [8.718e-03,5.274e-02]
gamma[1] 0.0536 1.197e-02 4.479 7.486e-06 [3.016e-02,7.709e-02]
beta[1] 0.9379 1.185e-02 79.181 0.000 [ 0.915, 0.961]
============================================================================
Covariance estimator: robust
# Calculate the RMSE
rmse_arch = np.sqrt(mse(DBK_RET[-b:] / 100,
np.sqrt(Forecast_GJR_GARCH_DBK\
.variance.iloc[-len(DBK_10):]
/ 100)))
print('The RMSE value of GJR-GARCH model (DBK): {:.4f}'.format(rmse_arch))
The RMSE value of GJR-GARCH model (DBK): 0.2072
# Specify GARCH model assumptions
GJR_GARCH_BAC = arch_model(BAC_RET, p=1, o=1, q=1, dist = 'normal')
# Fit the model
GJR_GARCH_result_BAC = GJR_GARCH_BAC.fit(update_freq=5, disp="off")
# Get model estimated volatility
GJR_GARCH_vol_BAC = GJR_GARCH_result_BAC.conditional_volatility
# Forecasting
Prediction_GJR_GARCH_BAC = GJR_GARCH_result_BAC.forecast(horizon = 1, start = '2020-12-14')
Forecast_GJR_GARCH_BAC = Prediction_GJR_GARCH_BAC
Forecast_GJR_GARCH_BAC
print(GJR_GARCH_result_BAC.summary())
Constant Mean - GJR-GARCH Model Results
==============================================================================
Dep. Variable: Ret_BAC R-squared: 0.000
Mean Model: Constant Mean Adj. R-squared: 0.000
Vol Model: GJR-GARCH Log-Likelihood: -10116.7
Distribution: Normal AIC: 20243.3
Method: Maximum Likelihood BIC: 20275.9
No. Observations: 5008
Date: Thu, Jun 02 2022 Df Residuals: 5007
Time: 23:44:20 Df Model: 1
Mean Model
=============================================================================
coef std err t P>|t| 95.0% Conf. Int.
-----------------------------------------------------------------------------
mu 4.6621e-03 2.240e-02 0.208 0.835 [-3.923e-02,4.856e-02]
Volatility Model
============================================================================
coef std err t P>|t| 95.0% Conf. Int.
----------------------------------------------------------------------------
omega 0.0434 2.124e-02 2.041 4.124e-02 [1.724e-03,8.499e-02]
alpha[1] 0.0395 1.712e-02 2.306 2.113e-02 [5.916e-03,7.301e-02]
gamma[1] 0.0839 2.290e-02 3.664 2.478e-04 [3.903e-02, 0.129]
beta[1] 0.9108 2.629e-02 34.652 4.172e-263 [ 0.859, 0.962]
============================================================================
Covariance estimator: robust
# Calculate the RMSE
rmse_arch = np.sqrt(mse(BAC_RET[-d:] / 100,
np.sqrt(Forecast_GJR_GARCH_BAC\
.variance.iloc[-len(BAC_10):]
/ 100)))
print('The RMSE value of GJR-GARCH model (BAC): {:.4f}'.format(rmse_arch))
The RMSE value of GJR-GARCH model (BAC): 0.1787
# Ploting Returns/Conditional Variance
## DBK
figure(figsize=(20, 6), dpi=100)
plt.plot(GJR_GARCH_vol_DBK, color = 'red', label = 'Conditional Variance')
plt.plot(DBK_RET, color = 'blue',
label = 'Daily Returns', alpha = 0.4)
plt.legend(loc = 'upper right')
plt.title('GJR-GARCH: Returns/Conditional Variance (DBK)', fontsize=16)
plt.show()
## BAC
figure(figsize=(20, 6), dpi=100)
plt.plot(GJR_GARCH_vol_BAC, color = 'red', label = 'Conditional Variance')
plt.plot(BAC_RET, color = 'blue',
label = 'Daily Returns', alpha = 0.4)
plt.legend(loc = 'upper right')
plt.title('GJR-GARCH: Returns/Conditional Variance (BAC)', fontsize=16)
plt.show()
# Ploting Actual Returns/Predicted Returns
## DBK
plt.figure(figsize=(20, 6))
plt.plot(DBK_RET.iloc[-len(DBK_90):] / 100, label='Actual Returns')
plt.plot(Forecast_GJR_GARCH_DBK.variance.iloc[-len(DBK_10):] / 100,
label='Predicted Returns')
plt.title('GJR-GARCH: Actual Returns/Predicted Returns (DBK)', fontsize=16)
plt.legend()
plt.show()
## BAC
plt.figure(figsize=(20, 6))
plt.plot(BAC_RET.iloc[-len(BAC_90):] / 100, label='Actual Returns')
plt.plot(Forecast_GJR_GARCH_BAC.variance.iloc[-len(BAC_10):] / 100,
label='Predicted Returns')
plt.title('GJR-GARCH: Actual Returns/Predicted Returns (BAC)', fontsize=16)
plt.legend()
plt.show()
# Specify GARCH model assumptions
GJR_GARCH_BMW = arch_model(BMW_RET, p=1, o=1, q=1, dist = 'normal')
# Fit the model
GJR_GARCH_result_BMW = GJR_GARCH_BMW.fit(update_freq=5, disp="off")
# Get model estimated volatility
GJR_GARCH_vol_BMW = GJR_GARCH_result_BMW.conditional_volatility
# Forecasting
Prediction_GJR_GARCH_BMW = GJR_GARCH_result_BMW.forecast(horizon = 1, start = '2020-12-14')
Forecast_GJR_GARCH_BMW = Prediction_GJR_GARCH_BMW
Forecast_GJR_GARCH_BMW
print(GJR_GARCH_result_BMW.summary())
Constant Mean - GJR-GARCH Model Results
==============================================================================
Dep. Variable: Ret_BMW R-squared: 0.000
Mean Model: Constant Mean Adj. R-squared: 0.000
Vol Model: GJR-GARCH Log-Likelihood: -10085.6
Distribution: Normal AIC: 20181.2
Method: Maximum Likelihood BIC: 20213.8
No. Observations: 5008
Date: Thu, Jun 02 2022 Df Residuals: 5007
Time: 23:44:22 Df Model: 1
Mean Model
=============================================================================
coef std err t P>|t| 95.0% Conf. Int.
-----------------------------------------------------------------------------
mu 3.6091e-03 2.275e-02 0.159 0.874 [-4.098e-02,4.820e-02]
Volatility Model
============================================================================
coef std err t P>|t| 95.0% Conf. Int.
----------------------------------------------------------------------------
omega 0.0340 1.134e-02 3.000 2.698e-03 [1.180e-02,5.627e-02]
alpha[1] 0.0236 7.849e-03 3.013 2.587e-03 [8.264e-03,3.903e-02]
gamma[1] 0.0637 1.476e-02 4.317 1.583e-05 [3.478e-02,9.263e-02]
beta[1] 0.9368 1.148e-02 81.625 0.000 [ 0.914, 0.959]
============================================================================
Covariance estimator: robust
# Calculate the RMSE
rmse_arch = np.sqrt(mse(BMW_RET[-f:] / 100,
np.sqrt(Forecast_GJR_GARCH_BMW\
.variance.iloc[-len(BMW_10):]
/ 100)))
print('The RMSE value of GJR-GARCH model (BMW): {:.4f}'.format(rmse_arch))
The RMSE value of GJR-GARCH model (BMW): 0.1722
# Specify GARCH model assumptions
GJR_GARCH_F = arch_model(F_RET, p=1, o=1, q=1, dist = 'normal')
# Fit the model
GJR_GARCH_result_F = GJR_GARCH_F.fit(update_freq=5, disp="off")
# Get model estimated volatility
GJR_GARCH_vol_F = GJR_GARCH_result_F.conditional_volatility
# Forecasting
Prediction_GJR_GARCH_F = GJR_GARCH_result_F.forecast(horizon = 1, start = '2020-12-14')
Forecast_GJR_GARCH_F = Prediction_GJR_GARCH_F
Forecast_GJR_GARCH_F
print(GJR_GARCH_result_F.summary())
Constant Mean - GJR-GARCH Model Results
==============================================================================
Dep. Variable: Ret_F R-squared: 0.000
Mean Model: Constant Mean Adj. R-squared: 0.000
Vol Model: GJR-GARCH Log-Likelihood: -10943.6
Distribution: Normal AIC: 21897.3
Method: Maximum Likelihood BIC: 21929.8
No. Observations: 5008
Date: Thu, Jun 02 2022 Df Residuals: 5007
Time: 23:44:22 Df Model: 1
Mean Model
===========================================================================
coef std err t P>|t| 95.0% Conf. Int.
---------------------------------------------------------------------------
mu -0.0540 2.744e-02 -1.967 4.920e-02 [ -0.108,-1.888e-04]
Volatility Model
=============================================================================
coef std err t P>|t| 95.0% Conf. Int.
-----------------------------------------------------------------------------
omega 0.0630 3.864e-02 1.630 0.103 [-1.275e-02, 0.139]
alpha[1] 0.0516 2.526e-02 2.043 4.106e-02 [2.094e-03, 0.101]
gamma[1] 0.0184 1.284e-02 1.430 0.153 [-6.807e-03,4.351e-02]
beta[1] 0.9288 2.771e-02 33.516 2.856e-246 [ 0.875, 0.983]
=============================================================================
Covariance estimator: robust
# Calculate the RMSE
rmse_arch = np.sqrt(mse(F_RET[-h:] / 100,
np.sqrt(Forecast_GJR_GARCH_F\
.variance.iloc[-len(F_10):]
/ 100)))
print('The RMSE value of GJR-GARCH model (FORD): {:.4f}'.format(rmse_arch))
The RMSE value of GJR-GARCH model (FORD): 0.2156
# Ploting Returns/Conditional Variance
## BMW
figure(figsize=(20, 6), dpi=100)
plt.plot(GJR_GARCH_vol_BMW, color = 'red', label = 'Conditional Variance')
plt.plot(BMW_RET, color = 'blue',
label = 'Daily Returns', alpha = 0.4)
plt.legend(loc = 'upper right')
plt.title('GJR-GARCH: Returns/Conditional Variance (BMW)', fontsize=16)
plt.show()
## FORD
figure(figsize=(20, 6), dpi=100)
plt.plot(GJR_GARCH_vol_F, color = 'red', label = 'Conditional Variance')
plt.plot(F_RET, color = 'blue',
label = 'Daily Returns', alpha = 0.4)
plt.legend(loc = 'upper right')
plt.title('GJR-GARCH: Returns/Conditional Variance (FORD)', fontsize=16)
plt.show()
# Ploting Actual Returns/Predicted Returns
## BMW
plt.figure(figsize=(20, 6))
plt.plot(BMW_RET.iloc[-len(BMW_90):] / 100, label='Actual Returns')
plt.plot(Forecast_GJR_GARCH_BMW.variance.iloc[-len(BMW_10):] / 100,
label='Predicted Returns')
plt.title('GJR-GARCH: Actual Returns/Predicted Returns (BMW)', fontsize=16)
plt.legend()
plt.show()
## FORD
plt.figure(figsize=(20, 6))
plt.plot(F_RET.iloc[-len(F_90):] / 100, label='Actual Returns')
plt.plot(Forecast_GJR_GARCH_F.variance.iloc[-len(F_10):] / 100,
label='Predicted Returns')
plt.title('GJR-GARCH: Actual Returns/Predicted Returns (FORD)', fontsize=16)
plt.legend()
plt.show()
# transform a time series dataset into a supervised learning dataset (Input : Output)
## Adjust time format
def str_to_datetime(s):
split = s.split('-')
year, month, day = int(split[0]), int(split[1]), int(split[2])
return datetime.datetime(year=year, month=month, day=day)
datetime_object = str_to_datetime('2001-01-04')
## Create a function
def DBK_to_windowed_DBK(DBK_RET, first_date_str, last_date_str, n=3):
first_date = str_to_datetime(first_date_str)
last_date = str_to_datetime(last_date_str)
target_date = first_date
dates_DBK = []
X, Y = [], []
last_time = False
while True:
DBK_subset = DBK_RET.loc[:target_date].tail(n+1)
if len(DBK_subset) != n+1:
print(f'Error: Window of size {n} is too large for date {target_date}')
return
values = DBK_subset['Ret_DBK'].to_numpy()
x, y = values[:-1], values[-1]
dates_DBK.append(target_date)
X.append(x)
Y.append(y)
next_week = DBK_RET.loc[target_date:target_date+datetime.timedelta(days=7)]
next_datetime_str = str(next_week.head(2).tail(1).index.values[0])
next_date_str = next_datetime_str.split('T')[0]
year_month_day = next_date_str.split('-')
year, month, day = year_month_day
next_date = datetime.datetime(day=int(day), month=int(month), year=int(year))
if last_time:
break
target_date = next_date
if target_date == last_date:
last_time = True
ret_DBK = pd.DataFrame({})
ret_DBK['Target Date'] = dates_DBK
X = np.array(X)
for i in range(0, n):
X[:, i]
ret_DBK[f'Target-{n-i}'] = X[:, i]
ret_DBK['Target'] = Y
return ret_DBK
## Start day second time around: '2020-01-03'
windowed_DBK = DBK_to_windowed_DBK(DBK_RET,
'2020-01-03',
'2020-12-30',
n=3)
windowed_DBK.head(n=10)
| Target Date | Target-3 | Target-2 | Target-1 | Target | |
|---|---|---|---|---|---|
| 0 | 2020-01-03 | -0.761316 | -2.275915 | 0.159150 | -1.329061 |
| 1 | 2020-01-06 | -2.275915 | 0.159150 | -1.329061 | -0.900596 |
| 2 | 2020-01-07 | 0.159150 | -1.329061 | -0.900596 | 3.406856 |
| 3 | 2020-01-08 | -1.329061 | -0.900596 | 3.406856 | 3.320630 |
| 4 | 2020-01-09 | -0.900596 | 3.406856 | 3.320630 | 0.402574 |
| 5 | 2020-01-10 | 3.406856 | 3.320630 | 0.402574 | -0.272532 |
| 6 | 2020-01-13 | 3.320630 | 0.402574 | -0.272532 | -1.505760 |
| 7 | 2020-01-14 | 0.402574 | -0.272532 | -1.505760 | 2.256665 |
| 8 | 2020-01-15 | -0.272532 | -1.505760 | 2.256665 | -2.388675 |
| 9 | 2020-01-16 | -1.505760 | 2.256665 | -2.388675 | 0.776371 |
#Convert our new dataset into numpy arrays (to feed it directly into a tensorflow model)
def windowed_DBK_to_date_X_y(windowed_dataframe):
DBK_as_np = windowed_dataframe.to_numpy()
dates_DBK = DBK_as_np[:, 0]
middle_matrix = DBK_as_np[:, 1:-1]
X_DBK = middle_matrix.reshape((len(dates_DBK), middle_matrix.shape[1], 1))
Y_DBK = DBK_as_np[:, -1]
return dates_DBK, X_DBK.astype(np.float32), Y_DBK.astype(np.float32)
dates_DBK, X_DBK, y_DBK = windowed_DBK_to_date_X_y(windowed_DBK)
dates_DBK.shape, X_DBK.shape, y_DBK.shape
((237,), (237, 3, 1), (237,))
# Split the data into training, validation and testing partitions
q_85_DBK = int(len(dates_DBK) * .85)
q_95_DBK = int(len(dates_DBK) * .95)
dates_train_DBK, X_train_DBK, y_train_DBK = dates_DBK[:q_85_DBK], X_DBK[:q_85_DBK], y_DBK[:q_85_DBK]
dates_val_DBK, X_val_DBK, y_val_DBK = dates_DBK[q_85_DBK:q_95_DBK], X_DBK[q_85_DBK:q_95_DBK], y_DBK[q_85_DBK:q_95_DBK]
dates_test_DBK, X_test_DBK, y_test_DBK = dates_DBK[q_95_DBK:], X_DBK[q_95_DBK:], y_DBK[q_95_DBK:]
# Create & train the LSTM model
model_DBK = Sequential([layers.Input((3, 1)),
layers.LSTM(264),
layers.Dense(132, activation='relu'),
layers.Dense(132, activation='relu'),
layers.Dense(1)])
model_DBK.compile(loss='mse',
optimizer=Adam(learning_rate=0.001),
metrics=['mean_absolute_error'])
# Fitting the LSTM model
model_DBK.fit(X_train_DBK, y_train_DBK, validation_data=(X_val_DBK, y_val_DBK), epochs=1000)
# Forecasting
train_predictions_DBK = model_DBK.predict(X_train_DBK).flatten()
val_predictions_DBK = model_DBK.predict(X_val_DBK).flatten()
test_predictions_DBK = model_DBK.predict(X_test_DBK).flatten()
2022-06-02 23:44:24.609793: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
Epoch 1/1000 7/7 [==============================] - 4s 122ms/step - loss: 14.7010 - mean_absolute_error: 2.6141 - val_loss: 3.1132 - val_mean_absolute_error: 1.3688 Epoch 2/1000 7/7 [==============================] - 0s 26ms/step - loss: 14.5905 - mean_absolute_error: 2.6062 - val_loss: 3.0840 - val_mean_absolute_error: 1.3528 Epoch 3/1000 7/7 [==============================] - 0s 23ms/step - loss: 14.4811 - mean_absolute_error: 2.5987 - val_loss: 3.0932 - val_mean_absolute_error: 1.3597 Epoch 4/1000 7/7 [==============================] - 0s 25ms/step - loss: 14.3076 - mean_absolute_error: 2.5833 - val_loss: 3.0384 - val_mean_absolute_error: 1.3374 Epoch 5/1000 7/7 [==============================] - 0s 22ms/step - loss: 14.1136 - mean_absolute_error: 2.5686 - val_loss: 2.9454 - val_mean_absolute_error: 1.3032 Epoch 6/1000 7/7 [==============================] - 0s 21ms/step - loss: 13.9088 - mean_absolute_error: 2.5453 - val_loss: 2.8834 - val_mean_absolute_error: 1.2991 Epoch 7/1000 7/7 [==============================] - 0s 22ms/step - loss: 13.7648 - mean_absolute_error: 2.5306 - val_loss: 2.7628 - val_mean_absolute_error: 1.2890 Epoch 8/1000 7/7 [==============================] - 0s 23ms/step - loss: 13.5773 - mean_absolute_error: 2.5107 - val_loss: 2.6400 - val_mean_absolute_error: 1.2694 Epoch 9/1000 7/7 [==============================] - 0s 25ms/step - loss: 13.3618 - mean_absolute_error: 2.4755 - val_loss: 2.6227 - val_mean_absolute_error: 1.2631 Epoch 10/1000 7/7 [==============================] - 0s 27ms/step - loss: 13.1538 - mean_absolute_error: 2.4533 - val_loss: 2.6881 - val_mean_absolute_error: 1.2866 Epoch 11/1000 7/7 [==============================] - 0s 25ms/step - loss: 12.9437 - mean_absolute_error: 2.4376 - val_loss: 2.8791 - val_mean_absolute_error: 1.3257 Epoch 12/1000 7/7 [==============================] - 0s 27ms/step - loss: 12.7502 - mean_absolute_error: 2.4277 - val_loss: 2.7595 - val_mean_absolute_error: 1.3001 Epoch 13/1000 7/7 [==============================] - 0s 23ms/step - loss: 12.6433 - mean_absolute_error: 2.4077 - val_loss: 2.7993 - val_mean_absolute_error: 1.3203 Epoch 14/1000 7/7 [==============================] - 0s 29ms/step - loss: 12.4731 - mean_absolute_error: 2.3750 - val_loss: 2.7050 - val_mean_absolute_error: 1.2983 Epoch 15/1000 7/7 [==============================] - 0s 22ms/step - loss: 12.5268 - mean_absolute_error: 2.4167 - val_loss: 2.8794 - val_mean_absolute_error: 1.3378 Epoch 16/1000 7/7 [==============================] - 0s 22ms/step - loss: 12.2361 - mean_absolute_error: 2.3606 - val_loss: 2.7781 - val_mean_absolute_error: 1.3109 Epoch 17/1000 7/7 [==============================] - 0s 21ms/step - loss: 12.2948 - mean_absolute_error: 2.3431 - val_loss: 2.7946 - val_mean_absolute_error: 1.2975 Epoch 18/1000 7/7 [==============================] - 0s 24ms/step - loss: 11.6972 - mean_absolute_error: 2.3051 - val_loss: 2.8471 - val_mean_absolute_error: 1.3248 Epoch 19/1000 7/7 [==============================] - 0s 33ms/step - loss: 11.4407 - mean_absolute_error: 2.2980 - val_loss: 2.7638 - val_mean_absolute_error: 1.3069 Epoch 20/1000 7/7 [==============================] - 0s 26ms/step - loss: 11.1275 - mean_absolute_error: 2.2878 - val_loss: 2.8128 - val_mean_absolute_error: 1.3082 Epoch 21/1000 7/7 [==============================] - 0s 23ms/step - loss: 11.0212 - mean_absolute_error: 2.2731 - val_loss: 2.9761 - val_mean_absolute_error: 1.3750 Epoch 22/1000 7/7 [==============================] - 0s 24ms/step - loss: 10.8299 - mean_absolute_error: 2.2403 - val_loss: 2.8029 - val_mean_absolute_error: 1.3035 Epoch 23/1000 7/7 [==============================] - 0s 25ms/step - loss: 10.5139 - mean_absolute_error: 2.2383 - val_loss: 2.6976 - val_mean_absolute_error: 1.2761 Epoch 24/1000 7/7 [==============================] - 0s 26ms/step - loss: 10.1908 - mean_absolute_error: 2.2027 - val_loss: 2.8765 - val_mean_absolute_error: 1.3102 Epoch 25/1000 7/7 [==============================] - 0s 27ms/step - loss: 10.0326 - mean_absolute_error: 2.2165 - val_loss: 2.9509 - val_mean_absolute_error: 1.3407 Epoch 26/1000 7/7 [==============================] - 0s 24ms/step - loss: 9.8691 - mean_absolute_error: 2.2210 - val_loss: 2.8133 - val_mean_absolute_error: 1.2918 Epoch 27/1000 7/7 [==============================] - 0s 25ms/step - loss: 9.5506 - mean_absolute_error: 2.1833 - val_loss: 2.6727 - val_mean_absolute_error: 1.2467 Epoch 28/1000 7/7 [==============================] - 0s 21ms/step - loss: 9.3307 - mean_absolute_error: 2.1346 - val_loss: 2.6861 - val_mean_absolute_error: 1.2194 Epoch 29/1000 7/7 [==============================] - 0s 26ms/step - loss: 9.3968 - mean_absolute_error: 2.1445 - val_loss: 2.7305 - val_mean_absolute_error: 1.2429 Epoch 30/1000 7/7 [==============================] - 0s 27ms/step - loss: 9.1597 - mean_absolute_error: 2.1554 - val_loss: 2.6577 - val_mean_absolute_error: 1.2522 Epoch 31/1000 7/7 [==============================] - 0s 24ms/step - loss: 9.2594 - mean_absolute_error: 2.1797 - val_loss: 2.8002 - val_mean_absolute_error: 1.2681 Epoch 32/1000 7/7 [==============================] - 0s 28ms/step - loss: 9.3023 - mean_absolute_error: 2.1931 - val_loss: 2.7071 - val_mean_absolute_error: 1.2324 Epoch 33/1000 7/7 [==============================] - 0s 27ms/step - loss: 9.1654 - mean_absolute_error: 2.1399 - val_loss: 2.6461 - val_mean_absolute_error: 1.2169 Epoch 34/1000 7/7 [==============================] - 0s 26ms/step - loss: 9.5702 - mean_absolute_error: 2.1809 - val_loss: 2.6669 - val_mean_absolute_error: 1.2198 Epoch 35/1000 7/7 [==============================] - 0s 24ms/step - loss: 10.5184 - mean_absolute_error: 2.2704 - val_loss: 2.8517 - val_mean_absolute_error: 1.3055 Epoch 36/1000 7/7 [==============================] - 0s 23ms/step - loss: 9.0346 - mean_absolute_error: 2.1895 - val_loss: 2.9752 - val_mean_absolute_error: 1.3545 Epoch 37/1000 7/7 [==============================] - 0s 25ms/step - loss: 9.3243 - mean_absolute_error: 2.1902 - val_loss: 2.9717 - val_mean_absolute_error: 1.3441 Epoch 38/1000 7/7 [==============================] - 0s 22ms/step - loss: 8.8970 - mean_absolute_error: 2.1239 - val_loss: 2.8492 - val_mean_absolute_error: 1.2746 Epoch 39/1000 7/7 [==============================] - 0s 31ms/step - loss: 9.3469 - mean_absolute_error: 2.1962 - val_loss: 2.8221 - val_mean_absolute_error: 1.2798 Epoch 40/1000 7/7 [==============================] - 0s 22ms/step - loss: 8.4664 - mean_absolute_error: 2.0592 - val_loss: 2.8088 - val_mean_absolute_error: 1.2965 Epoch 41/1000 7/7 [==============================] - 0s 24ms/step - loss: 8.7978 - mean_absolute_error: 2.1207 - val_loss: 2.9737 - val_mean_absolute_error: 1.3438 Epoch 42/1000 7/7 [==============================] - 0s 19ms/step - loss: 8.6293 - mean_absolute_error: 2.1197 - val_loss: 2.9427 - val_mean_absolute_error: 1.3143 Epoch 43/1000 7/7 [==============================] - 0s 19ms/step - loss: 8.9756 - mean_absolute_error: 2.1472 - val_loss: 2.7333 - val_mean_absolute_error: 1.2326 Epoch 44/1000 7/7 [==============================] - 0s 36ms/step - loss: 9.0886 - mean_absolute_error: 2.1485 - val_loss: 3.1640 - val_mean_absolute_error: 1.4389 Epoch 45/1000 7/7 [==============================] - 0s 22ms/step - loss: 8.6756 - mean_absolute_error: 2.1585 - val_loss: 3.3187 - val_mean_absolute_error: 1.4421 Epoch 46/1000 7/7 [==============================] - 0s 22ms/step - loss: 8.8070 - mean_absolute_error: 2.1739 - val_loss: 2.7803 - val_mean_absolute_error: 1.2368 Epoch 47/1000 7/7 [==============================] - 0s 26ms/step - loss: 8.2111 - mean_absolute_error: 2.0360 - val_loss: 2.6428 - val_mean_absolute_error: 1.2269 Epoch 48/1000 7/7 [==============================] - 0s 22ms/step - loss: 8.4390 - mean_absolute_error: 2.0674 - val_loss: 2.6741 - val_mean_absolute_error: 1.2222 Epoch 49/1000 7/7 [==============================] - 0s 20ms/step - loss: 8.0463 - mean_absolute_error: 2.0291 - val_loss: 2.7935 - val_mean_absolute_error: 1.2538 Epoch 50/1000 7/7 [==============================] - 0s 21ms/step - loss: 8.4353 - mean_absolute_error: 2.0635 - val_loss: 2.8202 - val_mean_absolute_error: 1.3002 Epoch 51/1000 7/7 [==============================] - 0s 22ms/step - loss: 8.0048 - mean_absolute_error: 2.0159 - val_loss: 2.7672 - val_mean_absolute_error: 1.2758 Epoch 52/1000 7/7 [==============================] - 0s 21ms/step - loss: 8.2423 - mean_absolute_error: 2.0720 - val_loss: 2.7993 - val_mean_absolute_error: 1.2875 Epoch 53/1000 7/7 [==============================] - 0s 27ms/step - loss: 8.0038 - mean_absolute_error: 2.0101 - val_loss: 2.6769 - val_mean_absolute_error: 1.2558 Epoch 54/1000 7/7 [==============================] - 0s 19ms/step - loss: 8.3180 - mean_absolute_error: 2.0636 - val_loss: 3.0298 - val_mean_absolute_error: 1.3974 Epoch 55/1000 7/7 [==============================] - 0s 18ms/step - loss: 8.0118 - mean_absolute_error: 2.0570 - val_loss: 2.9543 - val_mean_absolute_error: 1.2798 Epoch 56/1000 7/7 [==============================] - 0s 19ms/step - loss: 8.1015 - mean_absolute_error: 2.0753 - val_loss: 2.7571 - val_mean_absolute_error: 1.2543 Epoch 57/1000 7/7 [==============================] - 0s 18ms/step - loss: 8.0593 - mean_absolute_error: 2.0492 - val_loss: 2.6849 - val_mean_absolute_error: 1.2606 Epoch 58/1000 7/7 [==============================] - 0s 18ms/step - loss: 7.9592 - mean_absolute_error: 2.0692 - val_loss: 3.1558 - val_mean_absolute_error: 1.4066 Epoch 59/1000 7/7 [==============================] - 0s 18ms/step - loss: 7.9292 - mean_absolute_error: 2.0245 - val_loss: 2.6209 - val_mean_absolute_error: 1.2645 Epoch 60/1000 7/7 [==============================] - 0s 19ms/step - loss: 7.9834 - mean_absolute_error: 2.0509 - val_loss: 3.0405 - val_mean_absolute_error: 1.3803 Epoch 61/1000 7/7 [==============================] - 0s 23ms/step - loss: 8.1140 - mean_absolute_error: 2.1146 - val_loss: 2.9636 - val_mean_absolute_error: 1.3245 Epoch 62/1000 7/7 [==============================] - 0s 17ms/step - loss: 8.0367 - mean_absolute_error: 2.0719 - val_loss: 2.7207 - val_mean_absolute_error: 1.2574 Epoch 63/1000 7/7 [==============================] - 0s 17ms/step - loss: 8.8481 - mean_absolute_error: 2.1445 - val_loss: 3.4484 - val_mean_absolute_error: 1.4705 Epoch 64/1000 7/7 [==============================] - 0s 18ms/step - loss: 7.6487 - mean_absolute_error: 2.0116 - val_loss: 2.5856 - val_mean_absolute_error: 1.2618 Epoch 65/1000 7/7 [==============================] - 0s 19ms/step - loss: 10.0826 - mean_absolute_error: 2.1529 - val_loss: 2.7362 - val_mean_absolute_error: 1.2553 Epoch 66/1000 7/7 [==============================] - 0s 18ms/step - loss: 8.2921 - mean_absolute_error: 2.1083 - val_loss: 3.1968 - val_mean_absolute_error: 1.3901 Epoch 67/1000 7/7 [==============================] - 0s 19ms/step - loss: 8.5165 - mean_absolute_error: 2.1666 - val_loss: 2.9898 - val_mean_absolute_error: 1.3914 Epoch 68/1000 7/7 [==============================] - 0s 18ms/step - loss: 7.7861 - mean_absolute_error: 1.9928 - val_loss: 2.7144 - val_mean_absolute_error: 1.2392 Epoch 69/1000 7/7 [==============================] - 0s 19ms/step - loss: 7.8428 - mean_absolute_error: 2.0645 - val_loss: 3.0669 - val_mean_absolute_error: 1.3276 Epoch 70/1000 7/7 [==============================] - 0s 19ms/step - loss: 7.4549 - mean_absolute_error: 1.9991 - val_loss: 3.1344 - val_mean_absolute_error: 1.3366 Epoch 71/1000 7/7 [==============================] - 0s 17ms/step - loss: 7.6113 - mean_absolute_error: 2.0119 - val_loss: 2.6552 - val_mean_absolute_error: 1.2498 Epoch 72/1000 7/7 [==============================] - 0s 18ms/step - loss: 7.2583 - mean_absolute_error: 1.9664 - val_loss: 2.6118 - val_mean_absolute_error: 1.2365 Epoch 73/1000 7/7 [==============================] - 0s 19ms/step - loss: 7.3354 - mean_absolute_error: 1.9656 - val_loss: 2.8790 - val_mean_absolute_error: 1.2790 Epoch 74/1000 7/7 [==============================] - 0s 17ms/step - loss: 7.2584 - mean_absolute_error: 1.9988 - val_loss: 2.8186 - val_mean_absolute_error: 1.2729 Epoch 75/1000 7/7 [==============================] - 0s 18ms/step - loss: 7.1789 - mean_absolute_error: 1.9766 - val_loss: 2.5574 - val_mean_absolute_error: 1.2374 Epoch 76/1000 7/7 [==============================] - 0s 18ms/step - loss: 7.3274 - mean_absolute_error: 1.9959 - val_loss: 2.6957 - val_mean_absolute_error: 1.1920 Epoch 77/1000 7/7 [==============================] - 0s 19ms/step - loss: 7.4422 - mean_absolute_error: 2.0053 - val_loss: 2.9084 - val_mean_absolute_error: 1.2959 Epoch 78/1000 7/7 [==============================] - 0s 18ms/step - loss: 7.3011 - mean_absolute_error: 1.9691 - val_loss: 2.5361 - val_mean_absolute_error: 1.1608 Epoch 79/1000 7/7 [==============================] - 0s 19ms/step - loss: 7.0239 - mean_absolute_error: 1.9202 - val_loss: 2.7487 - val_mean_absolute_error: 1.2695 Epoch 80/1000 7/7 [==============================] - 0s 18ms/step - loss: 7.1905 - mean_absolute_error: 1.9947 - val_loss: 2.9128 - val_mean_absolute_error: 1.3090 Epoch 81/1000 7/7 [==============================] - 0s 18ms/step - loss: 6.9760 - mean_absolute_error: 1.9562 - val_loss: 3.0182 - val_mean_absolute_error: 1.3158 Epoch 82/1000 7/7 [==============================] - 0s 19ms/step - loss: 7.3691 - mean_absolute_error: 1.9768 - val_loss: 2.7013 - val_mean_absolute_error: 1.2745 Epoch 83/1000 7/7 [==============================] - 0s 19ms/step - loss: 7.3182 - mean_absolute_error: 2.0302 - val_loss: 2.7599 - val_mean_absolute_error: 1.2691 Epoch 84/1000 7/7 [==============================] - 0s 18ms/step - loss: 7.1830 - mean_absolute_error: 2.0185 - val_loss: 3.0987 - val_mean_absolute_error: 1.2791 Epoch 85/1000 7/7 [==============================] - 0s 19ms/step - loss: 7.7322 - mean_absolute_error: 2.0385 - val_loss: 2.9574 - val_mean_absolute_error: 1.3227 Epoch 86/1000 7/7 [==============================] - 0s 19ms/step - loss: 6.8041 - mean_absolute_error: 1.9016 - val_loss: 2.8028 - val_mean_absolute_error: 1.3106 Epoch 87/1000 7/7 [==============================] - 0s 18ms/step - loss: 6.5304 - mean_absolute_error: 1.8559 - val_loss: 2.6141 - val_mean_absolute_error: 1.2373 Epoch 88/1000 7/7 [==============================] - 0s 19ms/step - loss: 6.6203 - mean_absolute_error: 1.8783 - val_loss: 2.5655 - val_mean_absolute_error: 1.2068 Epoch 89/1000 7/7 [==============================] - 0s 19ms/step - loss: 6.3693 - mean_absolute_error: 1.8270 - val_loss: 2.9241 - val_mean_absolute_error: 1.3380 Epoch 90/1000 7/7 [==============================] - 0s 19ms/step - loss: 6.4214 - mean_absolute_error: 1.8629 - val_loss: 2.7972 - val_mean_absolute_error: 1.2731 Epoch 91/1000 7/7 [==============================] - 0s 19ms/step - loss: 6.1055 - mean_absolute_error: 1.8158 - val_loss: 2.6345 - val_mean_absolute_error: 1.2246 Epoch 92/1000 7/7 [==============================] - 0s 18ms/step - loss: 6.3525 - mean_absolute_error: 1.8423 - val_loss: 2.6128 - val_mean_absolute_error: 1.2525 Epoch 93/1000 7/7 [==============================] - 0s 19ms/step - loss: 6.4716 - mean_absolute_error: 1.8672 - val_loss: 2.6659 - val_mean_absolute_error: 1.2497 Epoch 94/1000 7/7 [==============================] - 0s 18ms/step - loss: 6.0434 - mean_absolute_error: 1.8134 - val_loss: 2.5099 - val_mean_absolute_error: 1.1790 Epoch 95/1000 7/7 [==============================] - 0s 19ms/step - loss: 6.3622 - mean_absolute_error: 1.8280 - val_loss: 3.0618 - val_mean_absolute_error: 1.3883 Epoch 96/1000 7/7 [==============================] - 0s 18ms/step - loss: 6.2480 - mean_absolute_error: 1.8282 - val_loss: 2.6334 - val_mean_absolute_error: 1.2544 Epoch 97/1000 7/7 [==============================] - 0s 31ms/step - loss: 6.2471 - mean_absolute_error: 1.8199 - val_loss: 2.5608 - val_mean_absolute_error: 1.2218 Epoch 98/1000 7/7 [==============================] - 0s 19ms/step - loss: 6.0208 - mean_absolute_error: 1.8045 - val_loss: 3.0784 - val_mean_absolute_error: 1.3533 Epoch 99/1000 7/7 [==============================] - 0s 21ms/step - loss: 5.8965 - mean_absolute_error: 1.7710 - val_loss: 2.6663 - val_mean_absolute_error: 1.2560 Epoch 100/1000 7/7 [==============================] - 0s 19ms/step - loss: 6.0237 - mean_absolute_error: 1.8016 - val_loss: 2.6347 - val_mean_absolute_error: 1.2782 Epoch 101/1000 7/7 [==============================] - 0s 21ms/step - loss: 5.7459 - mean_absolute_error: 1.7713 - val_loss: 2.8096 - val_mean_absolute_error: 1.2843 Epoch 102/1000 7/7 [==============================] - 0s 18ms/step - loss: 5.7314 - mean_absolute_error: 1.7832 - val_loss: 2.7017 - val_mean_absolute_error: 1.2484 Epoch 103/1000 7/7 [==============================] - 0s 17ms/step - loss: 5.6315 - mean_absolute_error: 1.7391 - val_loss: 2.5796 - val_mean_absolute_error: 1.2028 Epoch 104/1000 7/7 [==============================] - 0s 18ms/step - loss: 5.4770 - mean_absolute_error: 1.7105 - val_loss: 2.7879 - val_mean_absolute_error: 1.2770 Epoch 105/1000 7/7 [==============================] - 0s 19ms/step - loss: 5.2666 - mean_absolute_error: 1.6739 - val_loss: 2.5124 - val_mean_absolute_error: 1.2259 Epoch 106/1000 7/7 [==============================] - 0s 18ms/step - loss: 5.3419 - mean_absolute_error: 1.6906 - val_loss: 2.6408 - val_mean_absolute_error: 1.2418 Epoch 107/1000 7/7 [==============================] - 0s 18ms/step - loss: 5.4645 - mean_absolute_error: 1.7092 - val_loss: 2.6619 - val_mean_absolute_error: 1.2681 Epoch 108/1000 7/7 [==============================] - 0s 18ms/step - loss: 5.2645 - mean_absolute_error: 1.6658 - val_loss: 2.5538 - val_mean_absolute_error: 1.2263 Epoch 109/1000 7/7 [==============================] - 0s 18ms/step - loss: 5.1953 - mean_absolute_error: 1.6646 - val_loss: 2.8876 - val_mean_absolute_error: 1.2989 Epoch 110/1000 7/7 [==============================] - 0s 18ms/step - loss: 5.2212 - mean_absolute_error: 1.7139 - val_loss: 3.5707 - val_mean_absolute_error: 1.4868 Epoch 111/1000 7/7 [==============================] - 0s 18ms/step - loss: 5.0561 - mean_absolute_error: 1.6732 - val_loss: 2.7440 - val_mean_absolute_error: 1.2651 Epoch 112/1000 7/7 [==============================] - 0s 28ms/step - loss: 5.3108 - mean_absolute_error: 1.7168 - val_loss: 2.9198 - val_mean_absolute_error: 1.3409 Epoch 113/1000 7/7 [==============================] - 0s 19ms/step - loss: 5.1120 - mean_absolute_error: 1.6776 - val_loss: 2.6409 - val_mean_absolute_error: 1.3248 Epoch 114/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.8390 - mean_absolute_error: 1.6304 - val_loss: 2.7399 - val_mean_absolute_error: 1.2364 Epoch 115/1000 7/7 [==============================] - 0s 18ms/step - loss: 4.8647 - mean_absolute_error: 1.6141 - val_loss: 2.5058 - val_mean_absolute_error: 1.2002 Epoch 116/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.6743 - mean_absolute_error: 1.6049 - val_loss: 2.8644 - val_mean_absolute_error: 1.3458 Epoch 117/1000 7/7 [==============================] - 0s 18ms/step - loss: 4.5079 - mean_absolute_error: 1.5503 - val_loss: 2.5867 - val_mean_absolute_error: 1.2726 Epoch 118/1000 7/7 [==============================] - 0s 20ms/step - loss: 4.7743 - mean_absolute_error: 1.5986 - val_loss: 3.0485 - val_mean_absolute_error: 1.3695 Epoch 119/1000 7/7 [==============================] - 0s 20ms/step - loss: 4.5067 - mean_absolute_error: 1.5534 - val_loss: 2.9417 - val_mean_absolute_error: 1.3545 Epoch 120/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.6130 - mean_absolute_error: 1.6234 - val_loss: 2.7331 - val_mean_absolute_error: 1.2576 Epoch 121/1000 7/7 [==============================] - 0s 18ms/step - loss: 4.3928 - mean_absolute_error: 1.5609 - val_loss: 2.7475 - val_mean_absolute_error: 1.2847 Epoch 122/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.7335 - mean_absolute_error: 1.6199 - val_loss: 2.8281 - val_mean_absolute_error: 1.3285 Epoch 123/1000 7/7 [==============================] - 0s 18ms/step - loss: 4.8328 - mean_absolute_error: 1.6693 - val_loss: 3.0595 - val_mean_absolute_error: 1.4146 Epoch 124/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.8841 - mean_absolute_error: 1.6539 - val_loss: 2.9671 - val_mean_absolute_error: 1.3545 Epoch 125/1000 7/7 [==============================] - 0s 19ms/step - loss: 5.5821 - mean_absolute_error: 1.7139 - val_loss: 2.7361 - val_mean_absolute_error: 1.3356 Epoch 126/1000 7/7 [==============================] - 0s 18ms/step - loss: 4.2563 - mean_absolute_error: 1.5453 - val_loss: 2.7290 - val_mean_absolute_error: 1.3315 Epoch 127/1000 7/7 [==============================] - 0s 18ms/step - loss: 4.4511 - mean_absolute_error: 1.6018 - val_loss: 2.5414 - val_mean_absolute_error: 1.2479 Epoch 128/1000 7/7 [==============================] - 0s 21ms/step - loss: 4.2449 - mean_absolute_error: 1.5200 - val_loss: 2.8443 - val_mean_absolute_error: 1.3718 Epoch 129/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.9002 - mean_absolute_error: 1.4500 - val_loss: 2.6487 - val_mean_absolute_error: 1.3504 Epoch 130/1000 7/7 [==============================] - 0s 18ms/step - loss: 3.9238 - mean_absolute_error: 1.4532 - val_loss: 3.4625 - val_mean_absolute_error: 1.5169 Epoch 131/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.3851 - mean_absolute_error: 1.5031 - val_loss: 3.0585 - val_mean_absolute_error: 1.3277 Epoch 132/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.9786 - mean_absolute_error: 1.4928 - val_loss: 3.0635 - val_mean_absolute_error: 1.4219 Epoch 133/1000 7/7 [==============================] - 0s 20ms/step - loss: 4.8175 - mean_absolute_error: 1.6401 - val_loss: 3.5255 - val_mean_absolute_error: 1.5531 Epoch 134/1000 7/7 [==============================] - 0s 19ms/step - loss: 5.0081 - mean_absolute_error: 1.6865 - val_loss: 3.1250 - val_mean_absolute_error: 1.4212 Epoch 135/1000 7/7 [==============================] - 0s 20ms/step - loss: 4.8461 - mean_absolute_error: 1.6288 - val_loss: 3.3158 - val_mean_absolute_error: 1.4351 Epoch 136/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.4165 - mean_absolute_error: 1.6146 - val_loss: 2.8824 - val_mean_absolute_error: 1.4571 Epoch 137/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.6930 - mean_absolute_error: 1.6529 - val_loss: 2.9420 - val_mean_absolute_error: 1.3630 Epoch 138/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.6792 - mean_absolute_error: 1.6081 - val_loss: 2.9556 - val_mean_absolute_error: 1.3646 Epoch 139/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.8883 - mean_absolute_error: 1.4678 - val_loss: 3.1207 - val_mean_absolute_error: 1.3740 Epoch 140/1000 7/7 [==============================] - 0s 18ms/step - loss: 3.6868 - mean_absolute_error: 1.4205 - val_loss: 2.7020 - val_mean_absolute_error: 1.3695 Epoch 141/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.2650 - mean_absolute_error: 1.3219 - val_loss: 2.5934 - val_mean_absolute_error: 1.2731 Epoch 142/1000 7/7 [==============================] - 0s 22ms/step - loss: 3.3403 - mean_absolute_error: 1.3235 - val_loss: 2.9063 - val_mean_absolute_error: 1.3399 Epoch 143/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.2158 - mean_absolute_error: 1.3140 - val_loss: 2.6665 - val_mean_absolute_error: 1.3148 Epoch 144/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.0480 - mean_absolute_error: 1.2660 - val_loss: 2.8573 - val_mean_absolute_error: 1.3691 Epoch 145/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.9485 - mean_absolute_error: 1.2393 - val_loss: 3.0516 - val_mean_absolute_error: 1.3434 Epoch 146/1000 7/7 [==============================] - 0s 20ms/step - loss: 2.9659 - mean_absolute_error: 1.2445 - val_loss: 2.9934 - val_mean_absolute_error: 1.3467 Epoch 147/1000 7/7 [==============================] - 0s 20ms/step - loss: 3.0631 - mean_absolute_error: 1.3186 - val_loss: 2.6478 - val_mean_absolute_error: 1.3201 Epoch 148/1000 7/7 [==============================] - 0s 20ms/step - loss: 3.1114 - mean_absolute_error: 1.2663 - val_loss: 3.6379 - val_mean_absolute_error: 1.5340 Epoch 149/1000 7/7 [==============================] - 0s 18ms/step - loss: 3.0884 - mean_absolute_error: 1.3212 - val_loss: 2.8049 - val_mean_absolute_error: 1.4325 Epoch 150/1000 7/7 [==============================] - 0s 20ms/step - loss: 2.8363 - mean_absolute_error: 1.2358 - val_loss: 3.3169 - val_mean_absolute_error: 1.4668 Epoch 151/1000 7/7 [==============================] - 0s 26ms/step - loss: 2.6729 - mean_absolute_error: 1.1838 - val_loss: 2.9302 - val_mean_absolute_error: 1.3998 Epoch 152/1000 7/7 [==============================] - 0s 18ms/step - loss: 2.7446 - mean_absolute_error: 1.2324 - val_loss: 3.3977 - val_mean_absolute_error: 1.4141 Epoch 153/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.9506 - mean_absolute_error: 1.2876 - val_loss: 2.8441 - val_mean_absolute_error: 1.4383 Epoch 154/1000 7/7 [==============================] - 0s 20ms/step - loss: 2.9484 - mean_absolute_error: 1.3083 - val_loss: 3.2088 - val_mean_absolute_error: 1.4183 Epoch 155/1000 7/7 [==============================] - 0s 18ms/step - loss: 2.6058 - mean_absolute_error: 1.1778 - val_loss: 3.1120 - val_mean_absolute_error: 1.4261 Epoch 156/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.5554 - mean_absolute_error: 1.1731 - val_loss: 3.0772 - val_mean_absolute_error: 1.4226 Epoch 157/1000 7/7 [==============================] - 0s 23ms/step - loss: 2.6185 - mean_absolute_error: 1.2043 - val_loss: 3.2104 - val_mean_absolute_error: 1.3592 Epoch 158/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.5716 - mean_absolute_error: 1.2006 - val_loss: 2.7534 - val_mean_absolute_error: 1.3527 Epoch 159/1000 7/7 [==============================] - 0s 18ms/step - loss: 2.2356 - mean_absolute_error: 1.0593 - val_loss: 3.0759 - val_mean_absolute_error: 1.3601 Epoch 160/1000 7/7 [==============================] - 0s 18ms/step - loss: 2.2451 - mean_absolute_error: 1.0558 - val_loss: 2.9861 - val_mean_absolute_error: 1.3660 Epoch 161/1000 7/7 [==============================] - 0s 18ms/step - loss: 2.2470 - mean_absolute_error: 1.0854 - val_loss: 3.2694 - val_mean_absolute_error: 1.4649 Epoch 162/1000 7/7 [==============================] - 0s 18ms/step - loss: 2.2160 - mean_absolute_error: 1.0540 - val_loss: 3.0227 - val_mean_absolute_error: 1.4179 Epoch 163/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.1663 - mean_absolute_error: 1.0807 - val_loss: 3.1560 - val_mean_absolute_error: 1.3338 Epoch 164/1000 7/7 [==============================] - 0s 23ms/step - loss: 2.5163 - mean_absolute_error: 1.1319 - val_loss: 3.1576 - val_mean_absolute_error: 1.3188 Epoch 165/1000 7/7 [==============================] - 0s 20ms/step - loss: 2.3733 - mean_absolute_error: 1.1207 - val_loss: 3.2860 - val_mean_absolute_error: 1.4393 Epoch 166/1000 7/7 [==============================] - 0s 18ms/step - loss: 2.3526 - mean_absolute_error: 1.1499 - val_loss: 3.1255 - val_mean_absolute_error: 1.4440 Epoch 167/1000 7/7 [==============================] - 0s 30ms/step - loss: 2.1023 - mean_absolute_error: 1.0941 - val_loss: 3.2813 - val_mean_absolute_error: 1.4594 Epoch 168/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.0739 - mean_absolute_error: 1.0389 - val_loss: 2.9536 - val_mean_absolute_error: 1.3843 Epoch 169/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.9847 - mean_absolute_error: 1.0108 - val_loss: 3.2445 - val_mean_absolute_error: 1.4056 Epoch 170/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.9609 - mean_absolute_error: 1.0381 - val_loss: 3.3627 - val_mean_absolute_error: 1.4288 Epoch 171/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.1059 - mean_absolute_error: 1.0698 - val_loss: 3.1819 - val_mean_absolute_error: 1.3884 Epoch 172/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.8485 - mean_absolute_error: 1.0134 - val_loss: 3.2004 - val_mean_absolute_error: 1.3965 Epoch 173/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.8170 - mean_absolute_error: 0.9807 - val_loss: 3.2937 - val_mean_absolute_error: 1.4713 Epoch 174/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.7262 - mean_absolute_error: 0.9549 - val_loss: 3.0867 - val_mean_absolute_error: 1.3357 Epoch 175/1000 7/7 [==============================] - 0s 17ms/step - loss: 1.8498 - mean_absolute_error: 1.0233 - val_loss: 3.4628 - val_mean_absolute_error: 1.5061 Epoch 176/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.9832 - mean_absolute_error: 1.0457 - val_loss: 3.8626 - val_mean_absolute_error: 1.5362 Epoch 177/1000 7/7 [==============================] - 0s 18ms/step - loss: 2.0470 - mean_absolute_error: 1.0527 - val_loss: 3.2168 - val_mean_absolute_error: 1.4650 Epoch 178/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.9582 - mean_absolute_error: 1.0605 - val_loss: 3.0945 - val_mean_absolute_error: 1.3691 Epoch 179/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.9787 - mean_absolute_error: 1.0571 - val_loss: 3.9308 - val_mean_absolute_error: 1.5330 Epoch 180/1000 7/7 [==============================] - 0s 20ms/step - loss: 2.3765 - mean_absolute_error: 1.1152 - val_loss: 3.4270 - val_mean_absolute_error: 1.4199 Epoch 181/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.3243 - mean_absolute_error: 1.0749 - val_loss: 3.9306 - val_mean_absolute_error: 1.5909 Epoch 182/1000 7/7 [==============================] - 0s 18ms/step - loss: 2.1950 - mean_absolute_error: 1.0872 - val_loss: 4.0634 - val_mean_absolute_error: 1.5382 Epoch 183/1000 7/7 [==============================] - 0s 17ms/step - loss: 1.9320 - mean_absolute_error: 1.0380 - val_loss: 3.3372 - val_mean_absolute_error: 1.4572 Epoch 184/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.0822 - mean_absolute_error: 1.0543 - val_loss: 3.2259 - val_mean_absolute_error: 1.3681 Epoch 185/1000 7/7 [==============================] - 0s 18ms/step - loss: 2.0612 - mean_absolute_error: 1.0590 - val_loss: 4.1611 - val_mean_absolute_error: 1.5214 Epoch 186/1000 7/7 [==============================] - 0s 27ms/step - loss: 2.3870 - mean_absolute_error: 1.1567 - val_loss: 3.1853 - val_mean_absolute_error: 1.4587 Epoch 187/1000 7/7 [==============================] - 0s 23ms/step - loss: 1.9633 - mean_absolute_error: 1.0072 - val_loss: 3.7987 - val_mean_absolute_error: 1.5349 Epoch 188/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.2816 - mean_absolute_error: 1.1152 - val_loss: 3.5092 - val_mean_absolute_error: 1.4569 Epoch 189/1000 7/7 [==============================] - 0s 18ms/step - loss: 2.2933 - mean_absolute_error: 1.1600 - val_loss: 3.6402 - val_mean_absolute_error: 1.5497 Epoch 190/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.7263 - mean_absolute_error: 0.9762 - val_loss: 2.9079 - val_mean_absolute_error: 1.2854 Epoch 191/1000 7/7 [==============================] - 0s 20ms/step - loss: 2.4778 - mean_absolute_error: 1.1989 - val_loss: 3.5070 - val_mean_absolute_error: 1.5427 Epoch 192/1000 7/7 [==============================] - 0s 25ms/step - loss: 2.5423 - mean_absolute_error: 1.2261 - val_loss: 3.2530 - val_mean_absolute_error: 1.3632 Epoch 193/1000 7/7 [==============================] - 0s 23ms/step - loss: 2.8444 - mean_absolute_error: 1.2739 - val_loss: 3.4547 - val_mean_absolute_error: 1.4518 Epoch 194/1000 7/7 [==============================] - 0s 22ms/step - loss: 2.5850 - mean_absolute_error: 1.1658 - val_loss: 3.1097 - val_mean_absolute_error: 1.3284 Epoch 195/1000 7/7 [==============================] - 0s 22ms/step - loss: 2.1699 - mean_absolute_error: 1.0780 - val_loss: 4.0743 - val_mean_absolute_error: 1.5642 Epoch 196/1000 7/7 [==============================] - 0s 23ms/step - loss: 1.9918 - mean_absolute_error: 1.0656 - val_loss: 2.9772 - val_mean_absolute_error: 1.4303 Epoch 197/1000 7/7 [==============================] - 0s 23ms/step - loss: 1.9889 - mean_absolute_error: 1.0785 - val_loss: 3.6677 - val_mean_absolute_error: 1.4930 Epoch 198/1000 7/7 [==============================] - 0s 26ms/step - loss: 1.8051 - mean_absolute_error: 0.9426 - val_loss: 2.8729 - val_mean_absolute_error: 1.3068 Epoch 199/1000 7/7 [==============================] - 0s 23ms/step - loss: 1.4327 - mean_absolute_error: 0.8728 - val_loss: 3.4649 - val_mean_absolute_error: 1.4668 Epoch 200/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.3610 - mean_absolute_error: 0.8444 - val_loss: 3.3743 - val_mean_absolute_error: 1.4088 Epoch 201/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.3004 - mean_absolute_error: 0.7976 - val_loss: 3.0542 - val_mean_absolute_error: 1.3672 Epoch 202/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.9670 - mean_absolute_error: 0.9862 - val_loss: 4.0389 - val_mean_absolute_error: 1.5477 Epoch 203/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.8449 - mean_absolute_error: 1.0414 - val_loss: 3.0936 - val_mean_absolute_error: 1.4228 Epoch 204/1000 7/7 [==============================] - 0s 18ms/step - loss: 2.1892 - mean_absolute_error: 1.1050 - val_loss: 4.0795 - val_mean_absolute_error: 1.4896 Epoch 205/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.7306 - mean_absolute_error: 1.0073 - val_loss: 2.9650 - val_mean_absolute_error: 1.4173 Epoch 206/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.5827 - mean_absolute_error: 0.9601 - val_loss: 3.5394 - val_mean_absolute_error: 1.4087 Epoch 207/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.3376 - mean_absolute_error: 0.8545 - val_loss: 3.2402 - val_mean_absolute_error: 1.4005 Epoch 208/1000 7/7 [==============================] - 0s 21ms/step - loss: 1.3227 - mean_absolute_error: 0.8404 - val_loss: 3.1991 - val_mean_absolute_error: 1.3804 Epoch 209/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.1226 - mean_absolute_error: 0.7620 - val_loss: 3.3614 - val_mean_absolute_error: 1.4791 Epoch 210/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.0512 - mean_absolute_error: 0.7051 - val_loss: 3.2661 - val_mean_absolute_error: 1.4266 Epoch 211/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.9860 - mean_absolute_error: 0.6917 - val_loss: 2.9803 - val_mean_absolute_error: 1.3470 Epoch 212/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.9597 - mean_absolute_error: 0.6753 - val_loss: 3.4206 - val_mean_absolute_error: 1.3957 Epoch 213/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.0006 - mean_absolute_error: 0.6974 - val_loss: 3.0160 - val_mean_absolute_error: 1.3522 Epoch 214/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.9549 - mean_absolute_error: 0.6871 - val_loss: 3.2720 - val_mean_absolute_error: 1.4377 Epoch 215/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.8999 - mean_absolute_error: 0.6240 - val_loss: 3.1905 - val_mean_absolute_error: 1.4043 Epoch 216/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.9196 - mean_absolute_error: 0.6421 - val_loss: 3.0814 - val_mean_absolute_error: 1.3354 Epoch 217/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.8838 - mean_absolute_error: 0.6559 - val_loss: 3.0420 - val_mean_absolute_error: 1.3921 Epoch 218/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.9385 - mean_absolute_error: 0.6788 - val_loss: 3.1166 - val_mean_absolute_error: 1.3884 Epoch 219/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.9036 - mean_absolute_error: 0.6591 - val_loss: 3.2219 - val_mean_absolute_error: 1.3301 Epoch 220/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.8314 - mean_absolute_error: 0.6439 - val_loss: 2.9704 - val_mean_absolute_error: 1.3610 Epoch 221/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.0475 - mean_absolute_error: 0.7236 - val_loss: 3.6529 - val_mean_absolute_error: 1.4182 Epoch 222/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.9258 - mean_absolute_error: 0.6721 - val_loss: 3.0645 - val_mean_absolute_error: 1.4575 Epoch 223/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.9273 - mean_absolute_error: 0.7084 - val_loss: 3.3298 - val_mean_absolute_error: 1.4113 Epoch 224/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.7786 - mean_absolute_error: 0.5930 - val_loss: 3.3645 - val_mean_absolute_error: 1.3865 Epoch 225/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.8682 - mean_absolute_error: 0.6625 - val_loss: 3.1756 - val_mean_absolute_error: 1.2845 Epoch 226/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.1242 - mean_absolute_error: 0.6996 - val_loss: 3.0216 - val_mean_absolute_error: 1.3093 Epoch 227/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.9503 - mean_absolute_error: 0.9860 - val_loss: 3.8004 - val_mean_absolute_error: 1.6181 Epoch 228/1000 7/7 [==============================] - 0s 17ms/step - loss: 1.2533 - mean_absolute_error: 0.8230 - val_loss: 2.7178 - val_mean_absolute_error: 1.1750 Epoch 229/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.2026 - mean_absolute_error: 0.7888 - val_loss: 2.9794 - val_mean_absolute_error: 1.3197 Epoch 230/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.8946 - mean_absolute_error: 0.6436 - val_loss: 3.3697 - val_mean_absolute_error: 1.3564 Epoch 231/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.8490 - mean_absolute_error: 0.6696 - val_loss: 3.0993 - val_mean_absolute_error: 1.4138 Epoch 232/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.2411 - mean_absolute_error: 0.7049 - val_loss: 3.5743 - val_mean_absolute_error: 1.2925 Epoch 233/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.4592 - mean_absolute_error: 0.9095 - val_loss: 3.9699 - val_mean_absolute_error: 1.6923 Epoch 234/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.0832 - mean_absolute_error: 0.9738 - val_loss: 3.9061 - val_mean_absolute_error: 1.5197 Epoch 235/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.5686 - mean_absolute_error: 0.8964 - val_loss: 3.9686 - val_mean_absolute_error: 1.6302 Epoch 236/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.6275 - mean_absolute_error: 0.9596 - val_loss: 3.7583 - val_mean_absolute_error: 1.4150 Epoch 237/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.9205 - mean_absolute_error: 0.9494 - val_loss: 3.3087 - val_mean_absolute_error: 1.4505 Epoch 238/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.4526 - mean_absolute_error: 0.8856 - val_loss: 3.4346 - val_mean_absolute_error: 1.4201 Epoch 239/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.0112 - mean_absolute_error: 0.7315 - val_loss: 2.7433 - val_mean_absolute_error: 1.3078 Epoch 240/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.1341 - mean_absolute_error: 0.7682 - val_loss: 2.8429 - val_mean_absolute_error: 1.3522 Epoch 241/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.9649 - mean_absolute_error: 0.6765 - val_loss: 3.8505 - val_mean_absolute_error: 1.4798 Epoch 242/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.9151 - mean_absolute_error: 0.7161 - val_loss: 2.9502 - val_mean_absolute_error: 1.3618 Epoch 243/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.8218 - mean_absolute_error: 0.6444 - val_loss: 3.1720 - val_mean_absolute_error: 1.4195 Epoch 244/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.7936 - mean_absolute_error: 0.6073 - val_loss: 3.3709 - val_mean_absolute_error: 1.4282 Epoch 245/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.7422 - mean_absolute_error: 0.5853 - val_loss: 3.0387 - val_mean_absolute_error: 1.2949 Epoch 246/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.6333 - mean_absolute_error: 0.5490 - val_loss: 3.2653 - val_mean_absolute_error: 1.3228 Epoch 247/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.6737 - mean_absolute_error: 0.5532 - val_loss: 3.2722 - val_mean_absolute_error: 1.4295 Epoch 248/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.5771 - mean_absolute_error: 0.5508 - val_loss: 3.4738 - val_mean_absolute_error: 1.3643 Epoch 249/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.5274 - mean_absolute_error: 0.5079 - val_loss: 3.2447 - val_mean_absolute_error: 1.3689 Epoch 250/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.5325 - mean_absolute_error: 0.5091 - val_loss: 3.2137 - val_mean_absolute_error: 1.3599 Epoch 251/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.5178 - mean_absolute_error: 0.4702 - val_loss: 3.2854 - val_mean_absolute_error: 1.3379 Epoch 252/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.5867 - mean_absolute_error: 0.5113 - val_loss: 3.0880 - val_mean_absolute_error: 1.4201 Epoch 253/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.7679 - mean_absolute_error: 0.5949 - val_loss: 3.5416 - val_mean_absolute_error: 1.3267 Epoch 254/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.6227 - mean_absolute_error: 0.5294 - val_loss: 3.3771 - val_mean_absolute_error: 1.4497 Epoch 255/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.5468 - mean_absolute_error: 0.5226 - val_loss: 2.9922 - val_mean_absolute_error: 1.2691 Epoch 256/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.5585 - mean_absolute_error: 0.5094 - val_loss: 3.5398 - val_mean_absolute_error: 1.4885 Epoch 257/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.5017 - mean_absolute_error: 0.4806 - val_loss: 3.3498 - val_mean_absolute_error: 1.2869 Epoch 258/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.7567 - mean_absolute_error: 0.5344 - val_loss: 3.3872 - val_mean_absolute_error: 1.3244 Epoch 259/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.6203 - mean_absolute_error: 0.4998 - val_loss: 3.3112 - val_mean_absolute_error: 1.3982 Epoch 260/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4858 - mean_absolute_error: 0.4752 - val_loss: 3.0888 - val_mean_absolute_error: 1.3719 Epoch 261/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.6231 - mean_absolute_error: 0.5180 - val_loss: 4.2465 - val_mean_absolute_error: 1.4853 Epoch 262/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.8466 - mean_absolute_error: 0.6445 - val_loss: 3.6284 - val_mean_absolute_error: 1.4528 Epoch 263/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.7000 - mean_absolute_error: 0.6062 - val_loss: 3.0870 - val_mean_absolute_error: 1.2932 Epoch 264/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.5390 - mean_absolute_error: 0.5124 - val_loss: 3.5302 - val_mean_absolute_error: 1.4152 Epoch 265/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.5309 - mean_absolute_error: 0.4900 - val_loss: 3.9173 - val_mean_absolute_error: 1.3984 Epoch 266/1000 7/7 [==============================] - 0s 23ms/step - loss: 1.0243 - mean_absolute_error: 0.6419 - val_loss: 3.3662 - val_mean_absolute_error: 1.3236 Epoch 267/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.9626 - mean_absolute_error: 0.6676 - val_loss: 3.5836 - val_mean_absolute_error: 1.5812 Epoch 268/1000 7/7 [==============================] - 0s 23ms/step - loss: 1.0945 - mean_absolute_error: 0.7256 - val_loss: 3.2049 - val_mean_absolute_error: 1.3222 Epoch 269/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.6177 - mean_absolute_error: 0.5345 - val_loss: 3.8826 - val_mean_absolute_error: 1.4220 Epoch 270/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.6350 - mean_absolute_error: 0.5444 - val_loss: 2.6638 - val_mean_absolute_error: 1.2589 Epoch 271/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.6067 - mean_absolute_error: 0.5655 - val_loss: 3.5458 - val_mean_absolute_error: 1.4026 Epoch 272/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.5799 - mean_absolute_error: 0.5576 - val_loss: 2.9358 - val_mean_absolute_error: 1.3582 Epoch 273/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.7543 - mean_absolute_error: 0.5666 - val_loss: 3.4521 - val_mean_absolute_error: 1.2993 Epoch 274/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.4657 - mean_absolute_error: 0.6953 - val_loss: 3.3959 - val_mean_absolute_error: 1.3437 Epoch 275/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.4855 - mean_absolute_error: 0.8819 - val_loss: 3.2553 - val_mean_absolute_error: 1.3267 Epoch 276/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.3821 - mean_absolute_error: 1.1470 - val_loss: 3.2409 - val_mean_absolute_error: 1.5310 Epoch 277/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.0812 - mean_absolute_error: 0.7827 - val_loss: 4.4474 - val_mean_absolute_error: 1.4840 Epoch 278/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.2295 - mean_absolute_error: 0.8177 - val_loss: 3.2408 - val_mean_absolute_error: 1.4114 Epoch 279/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.7283 - mean_absolute_error: 0.6522 - val_loss: 3.4143 - val_mean_absolute_error: 1.3659 Epoch 280/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.6615 - mean_absolute_error: 0.5925 - val_loss: 2.8128 - val_mean_absolute_error: 1.3225 Epoch 281/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.4700 - mean_absolute_error: 0.4815 - val_loss: 3.4676 - val_mean_absolute_error: 1.4242 Epoch 282/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.8232 - mean_absolute_error: 0.5513 - val_loss: 2.8044 - val_mean_absolute_error: 1.2116 Epoch 283/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5120 - mean_absolute_error: 0.5004 - val_loss: 3.1158 - val_mean_absolute_error: 1.4231 Epoch 284/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.4679 - mean_absolute_error: 0.4835 - val_loss: 3.1366 - val_mean_absolute_error: 1.2060 Epoch 285/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3878 - mean_absolute_error: 0.4276 - val_loss: 3.0512 - val_mean_absolute_error: 1.3744 Epoch 286/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3070 - mean_absolute_error: 0.3578 - val_loss: 3.4720 - val_mean_absolute_error: 1.3255 Epoch 287/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.3422 - mean_absolute_error: 0.3737 - val_loss: 3.1612 - val_mean_absolute_error: 1.3422 Epoch 288/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2873 - mean_absolute_error: 0.3459 - val_loss: 3.3597 - val_mean_absolute_error: 1.3008 Epoch 289/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3515 - mean_absolute_error: 0.3810 - val_loss: 3.0244 - val_mean_absolute_error: 1.3777 Epoch 290/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3528 - mean_absolute_error: 0.3853 - val_loss: 3.1509 - val_mean_absolute_error: 1.3122 Epoch 291/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3512 - mean_absolute_error: 0.3677 - val_loss: 3.6624 - val_mean_absolute_error: 1.4149 Epoch 292/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3055 - mean_absolute_error: 0.3683 - val_loss: 3.2907 - val_mean_absolute_error: 1.3404 Epoch 293/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3671 - mean_absolute_error: 0.3800 - val_loss: 3.4459 - val_mean_absolute_error: 1.4674 Epoch 294/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3756 - mean_absolute_error: 0.4166 - val_loss: 3.1500 - val_mean_absolute_error: 1.2844 Epoch 295/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3073 - mean_absolute_error: 0.3742 - val_loss: 3.3682 - val_mean_absolute_error: 1.4475 Epoch 296/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2852 - mean_absolute_error: 0.3511 - val_loss: 3.1928 - val_mean_absolute_error: 1.2892 Epoch 297/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2827 - mean_absolute_error: 0.3603 - val_loss: 3.0762 - val_mean_absolute_error: 1.3254 Epoch 298/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5654 - mean_absolute_error: 0.4549 - val_loss: 3.5268 - val_mean_absolute_error: 1.4963 Epoch 299/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.5871 - mean_absolute_error: 0.5166 - val_loss: 3.1930 - val_mean_absolute_error: 1.2658 Epoch 300/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3645 - mean_absolute_error: 0.4175 - val_loss: 3.2736 - val_mean_absolute_error: 1.4084 Epoch 301/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2922 - mean_absolute_error: 0.3756 - val_loss: 3.3593 - val_mean_absolute_error: 1.3192 Epoch 302/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.3047 - mean_absolute_error: 0.3551 - val_loss: 3.0560 - val_mean_absolute_error: 1.3866 Epoch 303/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3651 - mean_absolute_error: 0.3859 - val_loss: 3.1558 - val_mean_absolute_error: 1.3001 Epoch 304/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.3178 - mean_absolute_error: 0.3613 - val_loss: 3.4144 - val_mean_absolute_error: 1.3190 Epoch 305/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.4149 - mean_absolute_error: 0.4220 - val_loss: 3.1630 - val_mean_absolute_error: 1.4070 Epoch 306/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.3826 - mean_absolute_error: 0.4360 - val_loss: 3.3990 - val_mean_absolute_error: 1.3187 Epoch 307/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4590 - mean_absolute_error: 0.4522 - val_loss: 3.5574 - val_mean_absolute_error: 1.5278 Epoch 308/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3956 - mean_absolute_error: 0.4368 - val_loss: 3.4328 - val_mean_absolute_error: 1.3094 Epoch 309/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.4145 - mean_absolute_error: 0.4378 - val_loss: 3.8386 - val_mean_absolute_error: 1.5051 Epoch 310/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.4059 - mean_absolute_error: 0.4243 - val_loss: 3.2450 - val_mean_absolute_error: 1.3032 Epoch 311/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.3659 - mean_absolute_error: 0.4148 - val_loss: 3.2839 - val_mean_absolute_error: 1.3776 Epoch 312/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3135 - mean_absolute_error: 0.3839 - val_loss: 3.2932 - val_mean_absolute_error: 1.3298 Epoch 313/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.3164 - mean_absolute_error: 0.3708 - val_loss: 3.1125 - val_mean_absolute_error: 1.3587 Epoch 314/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2701 - mean_absolute_error: 0.3294 - val_loss: 3.2241 - val_mean_absolute_error: 1.2867 Epoch 315/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2289 - mean_absolute_error: 0.2891 - val_loss: 3.3248 - val_mean_absolute_error: 1.4094 Epoch 316/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1787 - mean_absolute_error: 0.2612 - val_loss: 3.2351 - val_mean_absolute_error: 1.2771 Epoch 317/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2493 - mean_absolute_error: 0.2973 - val_loss: 3.4122 - val_mean_absolute_error: 1.3565 Epoch 318/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.1901 - mean_absolute_error: 0.2682 - val_loss: 3.1955 - val_mean_absolute_error: 1.3812 Epoch 319/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1963 - mean_absolute_error: 0.2655 - val_loss: 3.3330 - val_mean_absolute_error: 1.3275 Epoch 320/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.1820 - mean_absolute_error: 0.2274 - val_loss: 3.3884 - val_mean_absolute_error: 1.3735 Epoch 321/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2185 - mean_absolute_error: 0.2595 - val_loss: 3.3765 - val_mean_absolute_error: 1.2880 Epoch 322/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1700 - mean_absolute_error: 0.2311 - val_loss: 3.3327 - val_mean_absolute_error: 1.3948 Epoch 323/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2063 - mean_absolute_error: 0.2390 - val_loss: 3.2502 - val_mean_absolute_error: 1.2725 Epoch 324/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1668 - mean_absolute_error: 0.2321 - val_loss: 3.2533 - val_mean_absolute_error: 1.3532 Epoch 325/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1928 - mean_absolute_error: 0.2388 - val_loss: 3.4159 - val_mean_absolute_error: 1.2935 Epoch 326/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1822 - mean_absolute_error: 0.2426 - val_loss: 3.3121 - val_mean_absolute_error: 1.3710 Epoch 327/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1818 - mean_absolute_error: 0.2572 - val_loss: 3.5793 - val_mean_absolute_error: 1.3508 Epoch 328/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.2562 - mean_absolute_error: 0.3156 - val_loss: 3.1585 - val_mean_absolute_error: 1.3045 Epoch 329/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.2024 - mean_absolute_error: 0.2914 - val_loss: 3.3244 - val_mean_absolute_error: 1.3511 Epoch 330/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2180 - mean_absolute_error: 0.2809 - val_loss: 3.4065 - val_mean_absolute_error: 1.3219 Epoch 331/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2249 - mean_absolute_error: 0.2938 - val_loss: 3.4519 - val_mean_absolute_error: 1.3558 Epoch 332/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1780 - mean_absolute_error: 0.2460 - val_loss: 3.3433 - val_mean_absolute_error: 1.3319 Epoch 333/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1729 - mean_absolute_error: 0.2345 - val_loss: 3.5053 - val_mean_absolute_error: 1.3150 Epoch 334/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.3370 - mean_absolute_error: 0.3220 - val_loss: 3.3289 - val_mean_absolute_error: 1.4258 Epoch 335/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3250 - mean_absolute_error: 0.3777 - val_loss: 3.4978 - val_mean_absolute_error: 1.2416 Epoch 336/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3727 - mean_absolute_error: 0.4180 - val_loss: 3.5591 - val_mean_absolute_error: 1.4753 Epoch 337/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.4075 - mean_absolute_error: 0.4217 - val_loss: 3.6708 - val_mean_absolute_error: 1.2744 Epoch 338/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3687 - mean_absolute_error: 0.4412 - val_loss: 3.4399 - val_mean_absolute_error: 1.5119 Epoch 339/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3152 - mean_absolute_error: 0.3858 - val_loss: 3.7105 - val_mean_absolute_error: 1.3466 Epoch 340/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4614 - mean_absolute_error: 0.4111 - val_loss: 3.5411 - val_mean_absolute_error: 1.5301 Epoch 341/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.8540 - mean_absolute_error: 0.5631 - val_loss: 3.7432 - val_mean_absolute_error: 1.3413 Epoch 342/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.6393 - mean_absolute_error: 0.5617 - val_loss: 3.8273 - val_mean_absolute_error: 1.6093 Epoch 343/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4848 - mean_absolute_error: 0.4867 - val_loss: 3.2801 - val_mean_absolute_error: 1.2219 Epoch 344/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4747 - mean_absolute_error: 0.4882 - val_loss: 3.4828 - val_mean_absolute_error: 1.4789 Epoch 345/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3983 - mean_absolute_error: 0.4474 - val_loss: 4.1539 - val_mean_absolute_error: 1.3341 Epoch 346/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3629 - mean_absolute_error: 0.4033 - val_loss: 3.2597 - val_mean_absolute_error: 1.4481 Epoch 347/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2740 - mean_absolute_error: 0.3225 - val_loss: 3.1886 - val_mean_absolute_error: 1.2623 Epoch 348/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2987 - mean_absolute_error: 0.3377 - val_loss: 3.5222 - val_mean_absolute_error: 1.4450 Epoch 349/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2084 - mean_absolute_error: 0.2770 - val_loss: 3.2544 - val_mean_absolute_error: 1.2622 Epoch 350/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2643 - mean_absolute_error: 0.3268 - val_loss: 3.5665 - val_mean_absolute_error: 1.4673 Epoch 351/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2544 - mean_absolute_error: 0.3294 - val_loss: 3.8077 - val_mean_absolute_error: 1.3372 Epoch 352/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2988 - mean_absolute_error: 0.3443 - val_loss: 3.1070 - val_mean_absolute_error: 1.3451 Epoch 353/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2308 - mean_absolute_error: 0.3034 - val_loss: 3.4008 - val_mean_absolute_error: 1.2469 Epoch 354/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2215 - mean_absolute_error: 0.2947 - val_loss: 3.4396 - val_mean_absolute_error: 1.3936 Epoch 355/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2110 - mean_absolute_error: 0.2780 - val_loss: 3.2983 - val_mean_absolute_error: 1.3836 Epoch 356/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.4320 - mean_absolute_error: 0.3647 - val_loss: 3.8123 - val_mean_absolute_error: 1.3432 Epoch 357/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.4047 - mean_absolute_error: 0.4096 - val_loss: 3.8968 - val_mean_absolute_error: 1.5930 Epoch 358/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2906 - mean_absolute_error: 0.3559 - val_loss: 3.3000 - val_mean_absolute_error: 1.2577 Epoch 359/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.2801 - mean_absolute_error: 0.3722 - val_loss: 3.4730 - val_mean_absolute_error: 1.4411 Epoch 360/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2650 - mean_absolute_error: 0.3534 - val_loss: 3.7021 - val_mean_absolute_error: 1.3048 Epoch 361/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2479 - mean_absolute_error: 0.3356 - val_loss: 3.3354 - val_mean_absolute_error: 1.4299 Epoch 362/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2830 - mean_absolute_error: 0.3670 - val_loss: 3.3734 - val_mean_absolute_error: 1.2980 Epoch 363/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3069 - mean_absolute_error: 0.3759 - val_loss: 3.4944 - val_mean_absolute_error: 1.4246 Epoch 364/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2559 - mean_absolute_error: 0.3185 - val_loss: 3.3389 - val_mean_absolute_error: 1.2923 Epoch 365/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2019 - mean_absolute_error: 0.2744 - val_loss: 3.4349 - val_mean_absolute_error: 1.3615 Epoch 366/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1940 - mean_absolute_error: 0.2573 - val_loss: 3.6467 - val_mean_absolute_error: 1.4147 Epoch 367/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2561 - mean_absolute_error: 0.3411 - val_loss: 3.4052 - val_mean_absolute_error: 1.3655 Epoch 368/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.4651 - mean_absolute_error: 0.3603 - val_loss: 3.5093 - val_mean_absolute_error: 1.4100 Epoch 369/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.3867 - mean_absolute_error: 0.3750 - val_loss: 3.3349 - val_mean_absolute_error: 1.2826 Epoch 370/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.3276 - mean_absolute_error: 0.3693 - val_loss: 4.3609 - val_mean_absolute_error: 1.5931 Epoch 371/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2797 - mean_absolute_error: 0.3640 - val_loss: 4.1271 - val_mean_absolute_error: 1.4678 Epoch 372/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2546 - mean_absolute_error: 0.3076 - val_loss: 3.5603 - val_mean_absolute_error: 1.4553 Epoch 373/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1831 - mean_absolute_error: 0.2472 - val_loss: 3.6370 - val_mean_absolute_error: 1.3686 Epoch 374/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1600 - mean_absolute_error: 0.2287 - val_loss: 3.5923 - val_mean_absolute_error: 1.4289 Epoch 375/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1361 - mean_absolute_error: 0.1928 - val_loss: 3.5484 - val_mean_absolute_error: 1.3780 Epoch 376/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1319 - mean_absolute_error: 0.1800 - val_loss: 3.6952 - val_mean_absolute_error: 1.4327 Epoch 377/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1414 - mean_absolute_error: 0.1668 - val_loss: 3.6839 - val_mean_absolute_error: 1.4014 Epoch 378/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1436 - mean_absolute_error: 0.1892 - val_loss: 3.4876 - val_mean_absolute_error: 1.4150 Epoch 379/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1405 - mean_absolute_error: 0.1655 - val_loss: 3.6525 - val_mean_absolute_error: 1.3921 Epoch 380/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1321 - mean_absolute_error: 0.1669 - val_loss: 3.6699 - val_mean_absolute_error: 1.4641 Epoch 381/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1222 - mean_absolute_error: 0.1915 - val_loss: 3.5112 - val_mean_absolute_error: 1.3573 Epoch 382/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1556 - mean_absolute_error: 0.1917 - val_loss: 3.7214 - val_mean_absolute_error: 1.4551 Epoch 383/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.4454 - mean_absolute_error: 0.3117 - val_loss: 3.7994 - val_mean_absolute_error: 1.3419 Epoch 384/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2855 - mean_absolute_error: 0.3187 - val_loss: 3.6856 - val_mean_absolute_error: 1.5050 Epoch 385/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.2548 - mean_absolute_error: 0.3208 - val_loss: 3.3969 - val_mean_absolute_error: 1.3079 Epoch 386/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2492 - mean_absolute_error: 0.3131 - val_loss: 3.5775 - val_mean_absolute_error: 1.3903 Epoch 387/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1834 - mean_absolute_error: 0.2653 - val_loss: 3.7488 - val_mean_absolute_error: 1.3242 Epoch 388/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2165 - mean_absolute_error: 0.2795 - val_loss: 3.3123 - val_mean_absolute_error: 1.4014 Epoch 389/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2327 - mean_absolute_error: 0.3196 - val_loss: 3.5658 - val_mean_absolute_error: 1.3864 Epoch 390/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1607 - mean_absolute_error: 0.2427 - val_loss: 3.6065 - val_mean_absolute_error: 1.3775 Epoch 391/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1581 - mean_absolute_error: 0.2444 - val_loss: 3.5514 - val_mean_absolute_error: 1.4551 Epoch 392/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2026 - mean_absolute_error: 0.2568 - val_loss: 3.6090 - val_mean_absolute_error: 1.3248 Epoch 393/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2418 - mean_absolute_error: 0.3228 - val_loss: 3.6334 - val_mean_absolute_error: 1.5182 Epoch 394/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2413 - mean_absolute_error: 0.3281 - val_loss: 3.8879 - val_mean_absolute_error: 1.3452 Epoch 395/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2755 - mean_absolute_error: 0.3723 - val_loss: 3.5265 - val_mean_absolute_error: 1.4561 Epoch 396/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2503 - mean_absolute_error: 0.3261 - val_loss: 3.6591 - val_mean_absolute_error: 1.3409 Epoch 397/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2533 - mean_absolute_error: 0.3250 - val_loss: 3.4209 - val_mean_absolute_error: 1.4138 Epoch 398/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2015 - mean_absolute_error: 0.2763 - val_loss: 3.6528 - val_mean_absolute_error: 1.4158 Epoch 399/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2190 - mean_absolute_error: 0.2846 - val_loss: 3.5076 - val_mean_absolute_error: 1.3329 Epoch 400/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1500 - mean_absolute_error: 0.2230 - val_loss: 3.3326 - val_mean_absolute_error: 1.3823 Epoch 401/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2106 - mean_absolute_error: 0.2335 - val_loss: 3.5012 - val_mean_absolute_error: 1.3611 Epoch 402/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1514 - mean_absolute_error: 0.2174 - val_loss: 3.5702 - val_mean_absolute_error: 1.3396 Epoch 403/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.1915 - mean_absolute_error: 0.2460 - val_loss: 3.5956 - val_mean_absolute_error: 1.4796 Epoch 404/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1775 - mean_absolute_error: 0.2682 - val_loss: 3.5325 - val_mean_absolute_error: 1.3202 Epoch 405/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.2209 - mean_absolute_error: 0.2765 - val_loss: 3.6674 - val_mean_absolute_error: 1.4368 Epoch 406/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.3061 - mean_absolute_error: 0.3183 - val_loss: 3.4252 - val_mean_absolute_error: 1.3203 Epoch 407/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2242 - mean_absolute_error: 0.3256 - val_loss: 3.3624 - val_mean_absolute_error: 1.3598 Epoch 408/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2144 - mean_absolute_error: 0.2902 - val_loss: 3.8687 - val_mean_absolute_error: 1.3986 Epoch 409/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5272 - mean_absolute_error: 0.4257 - val_loss: 3.8712 - val_mean_absolute_error: 1.3907 Epoch 410/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.4751 - mean_absolute_error: 0.4417 - val_loss: 3.2661 - val_mean_absolute_error: 1.3534 Epoch 411/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.3495 - mean_absolute_error: 0.3720 - val_loss: 3.6026 - val_mean_absolute_error: 1.4106 Epoch 412/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.3568 - mean_absolute_error: 0.3986 - val_loss: 3.9876 - val_mean_absolute_error: 1.4740 Epoch 413/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.4054 - mean_absolute_error: 0.4528 - val_loss: 3.1906 - val_mean_absolute_error: 1.2832 Epoch 414/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.4435 - mean_absolute_error: 0.4917 - val_loss: 3.5668 - val_mean_absolute_error: 1.4228 Epoch 415/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.4881 - mean_absolute_error: 0.4965 - val_loss: 3.5896 - val_mean_absolute_error: 1.3983 Epoch 416/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.5191 - mean_absolute_error: 0.5311 - val_loss: 4.1064 - val_mean_absolute_error: 1.5517 Epoch 417/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.5794 - mean_absolute_error: 0.5300 - val_loss: 3.0130 - val_mean_absolute_error: 1.3036 Epoch 418/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.6231 - mean_absolute_error: 0.5629 - val_loss: 3.3530 - val_mean_absolute_error: 1.4606 Epoch 419/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.6124 - mean_absolute_error: 0.5431 - val_loss: 4.4028 - val_mean_absolute_error: 1.5717 Epoch 420/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.0199 - mean_absolute_error: 0.7049 - val_loss: 3.6788 - val_mean_absolute_error: 1.3959 Epoch 421/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.8945 - mean_absolute_error: 0.7200 - val_loss: 3.6946 - val_mean_absolute_error: 1.5524 Epoch 422/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.7488 - mean_absolute_error: 0.6792 - val_loss: 3.5182 - val_mean_absolute_error: 1.3496 Epoch 423/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5926 - mean_absolute_error: 0.5864 - val_loss: 3.1489 - val_mean_absolute_error: 1.3469 Epoch 424/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.5717 - mean_absolute_error: 0.5342 - val_loss: 3.0093 - val_mean_absolute_error: 1.2077 Epoch 425/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.4879 - mean_absolute_error: 0.4962 - val_loss: 3.1260 - val_mean_absolute_error: 1.3089 Epoch 426/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3670 - mean_absolute_error: 0.4100 - val_loss: 3.2790 - val_mean_absolute_error: 1.4030 Epoch 427/1000 7/7 [==============================] - 0s 16ms/step - loss: 0.2380 - mean_absolute_error: 0.3350 - val_loss: 3.2924 - val_mean_absolute_error: 1.2941 Epoch 428/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1974 - mean_absolute_error: 0.2933 - val_loss: 3.4041 - val_mean_absolute_error: 1.3287 Epoch 429/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2999 - mean_absolute_error: 0.3052 - val_loss: 3.1237 - val_mean_absolute_error: 1.3919 Epoch 430/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2997 - mean_absolute_error: 0.3204 - val_loss: 3.6484 - val_mean_absolute_error: 1.2821 Epoch 431/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2438 - mean_absolute_error: 0.3284 - val_loss: 3.3358 - val_mean_absolute_error: 1.4534 Epoch 432/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2175 - mean_absolute_error: 0.2988 - val_loss: 3.1340 - val_mean_absolute_error: 1.2701 Epoch 433/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1683 - mean_absolute_error: 0.2590 - val_loss: 3.4556 - val_mean_absolute_error: 1.4458 Epoch 434/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1841 - mean_absolute_error: 0.2650 - val_loss: 3.2800 - val_mean_absolute_error: 1.2973 Epoch 435/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1567 - mean_absolute_error: 0.2367 - val_loss: 3.3576 - val_mean_absolute_error: 1.3708 Epoch 436/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1545 - mean_absolute_error: 0.2363 - val_loss: 3.3266 - val_mean_absolute_error: 1.2836 Epoch 437/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1467 - mean_absolute_error: 0.2282 - val_loss: 3.3110 - val_mean_absolute_error: 1.3656 Epoch 438/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1271 - mean_absolute_error: 0.1983 - val_loss: 3.2391 - val_mean_absolute_error: 1.3367 Epoch 439/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1241 - mean_absolute_error: 0.1760 - val_loss: 3.2784 - val_mean_absolute_error: 1.3196 Epoch 440/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1312 - mean_absolute_error: 0.1567 - val_loss: 3.3777 - val_mean_absolute_error: 1.3253 Epoch 441/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1196 - mean_absolute_error: 0.1581 - val_loss: 3.2797 - val_mean_absolute_error: 1.3728 Epoch 442/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1203 - mean_absolute_error: 0.1513 - val_loss: 3.2459 - val_mean_absolute_error: 1.3235 Epoch 443/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1045 - mean_absolute_error: 0.1431 - val_loss: 3.4067 - val_mean_absolute_error: 1.3475 Epoch 444/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0976 - mean_absolute_error: 0.1372 - val_loss: 3.2195 - val_mean_absolute_error: 1.3421 Epoch 445/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1406 - mean_absolute_error: 0.1622 - val_loss: 3.3094 - val_mean_absolute_error: 1.3251 Epoch 446/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1817 - mean_absolute_error: 0.1709 - val_loss: 3.3160 - val_mean_absolute_error: 1.3301 Epoch 447/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1885 - mean_absolute_error: 0.2127 - val_loss: 3.4864 - val_mean_absolute_error: 1.4209 Epoch 448/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1390 - mean_absolute_error: 0.2170 - val_loss: 3.3763 - val_mean_absolute_error: 1.3117 Epoch 449/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1679 - mean_absolute_error: 0.2106 - val_loss: 3.1762 - val_mean_absolute_error: 1.3351 Epoch 450/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1785 - mean_absolute_error: 0.2571 - val_loss: 3.4782 - val_mean_absolute_error: 1.3218 Epoch 451/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1453 - mean_absolute_error: 0.2147 - val_loss: 3.2667 - val_mean_absolute_error: 1.2980 Epoch 452/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1303 - mean_absolute_error: 0.2194 - val_loss: 3.3784 - val_mean_absolute_error: 1.4095 Epoch 453/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1458 - mean_absolute_error: 0.2391 - val_loss: 3.4816 - val_mean_absolute_error: 1.3250 Epoch 454/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.1768 - mean_absolute_error: 0.2490 - val_loss: 3.4560 - val_mean_absolute_error: 1.3619 Epoch 455/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2286 - mean_absolute_error: 0.2684 - val_loss: 3.2377 - val_mean_absolute_error: 1.3086 Epoch 456/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2353 - mean_absolute_error: 0.3216 - val_loss: 3.4430 - val_mean_absolute_error: 1.3927 Epoch 457/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.3462 - mean_absolute_error: 0.3228 - val_loss: 3.6668 - val_mean_absolute_error: 1.3998 Epoch 458/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2204 - mean_absolute_error: 0.2794 - val_loss: 3.4074 - val_mean_absolute_error: 1.4153 Epoch 459/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2249 - mean_absolute_error: 0.3057 - val_loss: 3.3388 - val_mean_absolute_error: 1.3278 Epoch 460/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2044 - mean_absolute_error: 0.2762 - val_loss: 3.4079 - val_mean_absolute_error: 1.3368 Epoch 461/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.1855 - mean_absolute_error: 0.2461 - val_loss: 3.3956 - val_mean_absolute_error: 1.3638 Epoch 462/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1231 - mean_absolute_error: 0.2163 - val_loss: 3.4553 - val_mean_absolute_error: 1.3295 Epoch 463/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1759 - mean_absolute_error: 0.2675 - val_loss: 3.1581 - val_mean_absolute_error: 1.3253 Epoch 464/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.2236 - mean_absolute_error: 0.2685 - val_loss: 3.1429 - val_mean_absolute_error: 1.3898 Epoch 465/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.3324 - mean_absolute_error: 0.3800 - val_loss: 3.3550 - val_mean_absolute_error: 1.2648 Epoch 466/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.4056 - mean_absolute_error: 0.3816 - val_loss: 3.9454 - val_mean_absolute_error: 1.5928 Epoch 467/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.4397 - mean_absolute_error: 0.4159 - val_loss: 3.8356 - val_mean_absolute_error: 1.2762 Epoch 468/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.4857 - mean_absolute_error: 0.4867 - val_loss: 3.7770 - val_mean_absolute_error: 1.6164 Epoch 469/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.8402 - mean_absolute_error: 0.6484 - val_loss: 3.9139 - val_mean_absolute_error: 1.2941 Epoch 470/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.8110 - mean_absolute_error: 0.6437 - val_loss: 3.0885 - val_mean_absolute_error: 1.3878 Epoch 471/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.6465 - mean_absolute_error: 0.5585 - val_loss: 3.1927 - val_mean_absolute_error: 1.2854 Epoch 472/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.4918 - mean_absolute_error: 0.4696 - val_loss: 3.4349 - val_mean_absolute_error: 1.4221 Epoch 473/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.5626 - mean_absolute_error: 0.5357 - val_loss: 4.0745 - val_mean_absolute_error: 1.4450 Epoch 474/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.4607 - mean_absolute_error: 0.5001 - val_loss: 3.6469 - val_mean_absolute_error: 1.4463 Epoch 475/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.4403 - mean_absolute_error: 0.4805 - val_loss: 3.0439 - val_mean_absolute_error: 1.4435 Epoch 476/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.4012 - mean_absolute_error: 0.4654 - val_loss: 3.8278 - val_mean_absolute_error: 1.4220 Epoch 477/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.4162 - mean_absolute_error: 0.4422 - val_loss: 3.1652 - val_mean_absolute_error: 1.2978 Epoch 478/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.3889 - mean_absolute_error: 0.4203 - val_loss: 3.4086 - val_mean_absolute_error: 1.3829 Epoch 479/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.3275 - mean_absolute_error: 0.3989 - val_loss: 3.3736 - val_mean_absolute_error: 1.3810 Epoch 480/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.2410 - mean_absolute_error: 0.3488 - val_loss: 3.4586 - val_mean_absolute_error: 1.3996 Epoch 481/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.2599 - mean_absolute_error: 0.3179 - val_loss: 3.4735 - val_mean_absolute_error: 1.3406 Epoch 482/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.3395 - mean_absolute_error: 0.3846 - val_loss: 3.2302 - val_mean_absolute_error: 1.4016 Epoch 483/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.2744 - mean_absolute_error: 0.3478 - val_loss: 3.1494 - val_mean_absolute_error: 1.2603 Epoch 484/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.3121 - mean_absolute_error: 0.3771 - val_loss: 3.7791 - val_mean_absolute_error: 1.3546 Epoch 485/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.2825 - mean_absolute_error: 0.3487 - val_loss: 3.8242 - val_mean_absolute_error: 1.4670 Epoch 486/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.8285 - mean_absolute_error: 0.5491 - val_loss: 2.9566 - val_mean_absolute_error: 1.3197 Epoch 487/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.8310 - mean_absolute_error: 0.6876 - val_loss: 3.8287 - val_mean_absolute_error: 1.4982 Epoch 488/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.8107 - mean_absolute_error: 0.6039 - val_loss: 4.1336 - val_mean_absolute_error: 1.4140 Epoch 489/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.6619 - mean_absolute_error: 0.5943 - val_loss: 3.4367 - val_mean_absolute_error: 1.4711 Epoch 490/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.6059 - mean_absolute_error: 0.5861 - val_loss: 3.2322 - val_mean_absolute_error: 1.3394 Epoch 491/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3623 - mean_absolute_error: 0.4519 - val_loss: 3.2331 - val_mean_absolute_error: 1.3223 Epoch 492/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.4105 - mean_absolute_error: 0.4263 - val_loss: 2.9743 - val_mean_absolute_error: 1.3488 Epoch 493/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3193 - mean_absolute_error: 0.4004 - val_loss: 3.3818 - val_mean_absolute_error: 1.3099 Epoch 494/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.2418 - mean_absolute_error: 0.3388 - val_loss: 3.1441 - val_mean_absolute_error: 1.3155 Epoch 495/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1961 - mean_absolute_error: 0.2908 - val_loss: 3.1732 - val_mean_absolute_error: 1.3556 Epoch 496/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1890 - mean_absolute_error: 0.2825 - val_loss: 3.2973 - val_mean_absolute_error: 1.3832 Epoch 497/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.1450 - mean_absolute_error: 0.2387 - val_loss: 3.4709 - val_mean_absolute_error: 1.3361 Epoch 498/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.1544 - mean_absolute_error: 0.2234 - val_loss: 3.3538 - val_mean_absolute_error: 1.3618 Epoch 499/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1203 - mean_absolute_error: 0.1906 - val_loss: 3.2486 - val_mean_absolute_error: 1.3493 Epoch 500/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1409 - mean_absolute_error: 0.1810 - val_loss: 3.3459 - val_mean_absolute_error: 1.2996 Epoch 501/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1967 - mean_absolute_error: 0.2184 - val_loss: 3.3873 - val_mean_absolute_error: 1.3835 Epoch 502/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2529 - mean_absolute_error: 0.3081 - val_loss: 3.4559 - val_mean_absolute_error: 1.3056 Epoch 503/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.4136 - mean_absolute_error: 0.3948 - val_loss: 3.4002 - val_mean_absolute_error: 1.4311 Epoch 504/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3038 - mean_absolute_error: 0.3888 - val_loss: 3.2934 - val_mean_absolute_error: 1.2698 Epoch 505/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2447 - mean_absolute_error: 0.3178 - val_loss: 3.1446 - val_mean_absolute_error: 1.3595 Epoch 506/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1823 - mean_absolute_error: 0.2711 - val_loss: 3.2414 - val_mean_absolute_error: 1.3413 Epoch 507/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1283 - mean_absolute_error: 0.2201 - val_loss: 3.4141 - val_mean_absolute_error: 1.3733 Epoch 508/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1572 - mean_absolute_error: 0.1787 - val_loss: 3.3249 - val_mean_absolute_error: 1.3334 Epoch 509/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1160 - mean_absolute_error: 0.1625 - val_loss: 3.2523 - val_mean_absolute_error: 1.3076 Epoch 510/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1316 - mean_absolute_error: 0.1959 - val_loss: 3.3583 - val_mean_absolute_error: 1.4181 Epoch 511/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3644 - mean_absolute_error: 0.3353 - val_loss: 3.4640 - val_mean_absolute_error: 1.2781 Epoch 512/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2778 - mean_absolute_error: 0.3440 - val_loss: 3.5367 - val_mean_absolute_error: 1.4991 Epoch 513/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1905 - mean_absolute_error: 0.3074 - val_loss: 3.3642 - val_mean_absolute_error: 1.2818 Epoch 514/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1934 - mean_absolute_error: 0.2693 - val_loss: 3.3443 - val_mean_absolute_error: 1.4116 Epoch 515/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1396 - mean_absolute_error: 0.2535 - val_loss: 3.3525 - val_mean_absolute_error: 1.2442 Epoch 516/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1714 - mean_absolute_error: 0.2846 - val_loss: 3.3972 - val_mean_absolute_error: 1.3980 Epoch 517/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1650 - mean_absolute_error: 0.2487 - val_loss: 3.2375 - val_mean_absolute_error: 1.3271 Epoch 518/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1771 - mean_absolute_error: 0.2791 - val_loss: 3.4860 - val_mean_absolute_error: 1.3781 Epoch 519/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1257 - mean_absolute_error: 0.2219 - val_loss: 3.3729 - val_mean_absolute_error: 1.3164 Epoch 520/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1321 - mean_absolute_error: 0.2152 - val_loss: 3.3033 - val_mean_absolute_error: 1.3714 Epoch 521/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2257 - mean_absolute_error: 0.2582 - val_loss: 3.4965 - val_mean_absolute_error: 1.3568 Epoch 522/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1395 - mean_absolute_error: 0.2403 - val_loss: 3.2492 - val_mean_absolute_error: 1.3801 Epoch 523/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.1803 - mean_absolute_error: 0.2459 - val_loss: 3.2633 - val_mean_absolute_error: 1.3205 Epoch 524/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1825 - mean_absolute_error: 0.2568 - val_loss: 3.3585 - val_mean_absolute_error: 1.2534 Epoch 525/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1714 - mean_absolute_error: 0.2406 - val_loss: 3.2533 - val_mean_absolute_error: 1.4137 Epoch 526/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1886 - mean_absolute_error: 0.2445 - val_loss: 3.4337 - val_mean_absolute_error: 1.3178 Epoch 527/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2050 - mean_absolute_error: 0.2563 - val_loss: 3.5004 - val_mean_absolute_error: 1.4498 Epoch 528/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.3155 - mean_absolute_error: 0.3219 - val_loss: 3.7045 - val_mean_absolute_error: 1.3216 Epoch 529/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2927 - mean_absolute_error: 0.3918 - val_loss: 3.5782 - val_mean_absolute_error: 1.5016 Epoch 530/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2857 - mean_absolute_error: 0.3838 - val_loss: 3.5878 - val_mean_absolute_error: 1.2802 Epoch 531/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3447 - mean_absolute_error: 0.3768 - val_loss: 3.8511 - val_mean_absolute_error: 1.5775 Epoch 532/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2945 - mean_absolute_error: 0.3845 - val_loss: 3.2761 - val_mean_absolute_error: 1.3040 Epoch 533/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.2914 - mean_absolute_error: 0.3738 - val_loss: 3.7027 - val_mean_absolute_error: 1.5133 Epoch 534/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.6078 - mean_absolute_error: 0.5123 - val_loss: 3.6389 - val_mean_absolute_error: 1.3616 Epoch 535/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.7296 - mean_absolute_error: 0.5615 - val_loss: 3.1468 - val_mean_absolute_error: 1.3693 Epoch 536/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.7217 - mean_absolute_error: 0.5187 - val_loss: 3.8391 - val_mean_absolute_error: 1.3729 Epoch 537/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.7446 - mean_absolute_error: 0.5854 - val_loss: 3.5842 - val_mean_absolute_error: 1.4029 Epoch 538/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.6917 - mean_absolute_error: 0.5342 - val_loss: 3.0033 - val_mean_absolute_error: 1.3251 Epoch 539/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.6302 - mean_absolute_error: 0.5062 - val_loss: 3.5936 - val_mean_absolute_error: 1.4395 Epoch 540/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.4923 - mean_absolute_error: 0.4434 - val_loss: 3.3690 - val_mean_absolute_error: 1.3693 Epoch 541/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4148 - mean_absolute_error: 0.4136 - val_loss: 3.2933 - val_mean_absolute_error: 1.2711 Epoch 542/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.4388 - mean_absolute_error: 0.4174 - val_loss: 3.0766 - val_mean_absolute_error: 1.3666 Epoch 543/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.6401 - mean_absolute_error: 0.5684 - val_loss: 4.1018 - val_mean_absolute_error: 1.5009 Epoch 544/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.8197 - mean_absolute_error: 0.6657 - val_loss: 3.1879 - val_mean_absolute_error: 1.4930 Epoch 545/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.6788 - mean_absolute_error: 0.5800 - val_loss: 3.7442 - val_mean_absolute_error: 1.3997 Epoch 546/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3934 - mean_absolute_error: 0.4713 - val_loss: 3.7799 - val_mean_absolute_error: 1.3705 Epoch 547/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4091 - mean_absolute_error: 0.4060 - val_loss: 3.5570 - val_mean_absolute_error: 1.4851 Epoch 548/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2910 - mean_absolute_error: 0.3651 - val_loss: 3.3152 - val_mean_absolute_error: 1.3007 Epoch 549/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2661 - mean_absolute_error: 0.3636 - val_loss: 2.8988 - val_mean_absolute_error: 1.3240 Epoch 550/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.3190 - mean_absolute_error: 0.3756 - val_loss: 3.5017 - val_mean_absolute_error: 1.3815 Epoch 551/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3762 - mean_absolute_error: 0.3596 - val_loss: 3.3906 - val_mean_absolute_error: 1.3525 Epoch 552/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2060 - mean_absolute_error: 0.3006 - val_loss: 3.3056 - val_mean_absolute_error: 1.4641 Epoch 553/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1870 - mean_absolute_error: 0.2672 - val_loss: 3.6817 - val_mean_absolute_error: 1.2935 Epoch 554/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3508 - mean_absolute_error: 0.3565 - val_loss: 3.4398 - val_mean_absolute_error: 1.4739 Epoch 555/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4157 - mean_absolute_error: 0.4052 - val_loss: 2.9965 - val_mean_absolute_error: 1.2074 Epoch 556/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3836 - mean_absolute_error: 0.3872 - val_loss: 3.6757 - val_mean_absolute_error: 1.4382 Epoch 557/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3343 - mean_absolute_error: 0.4091 - val_loss: 3.3522 - val_mean_absolute_error: 1.2911 Epoch 558/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2998 - mean_absolute_error: 0.3661 - val_loss: 3.4871 - val_mean_absolute_error: 1.4566 Epoch 559/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2082 - mean_absolute_error: 0.3252 - val_loss: 3.2661 - val_mean_absolute_error: 1.3270 Epoch 560/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2100 - mean_absolute_error: 0.3068 - val_loss: 3.4511 - val_mean_absolute_error: 1.4950 Epoch 561/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1806 - mean_absolute_error: 0.2636 - val_loss: 3.9072 - val_mean_absolute_error: 1.3773 Epoch 562/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1828 - mean_absolute_error: 0.2856 - val_loss: 3.3342 - val_mean_absolute_error: 1.4919 Epoch 563/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1806 - mean_absolute_error: 0.2729 - val_loss: 3.5048 - val_mean_absolute_error: 1.3009 Epoch 564/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1307 - mean_absolute_error: 0.2227 - val_loss: 3.3369 - val_mean_absolute_error: 1.4442 Epoch 565/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1307 - mean_absolute_error: 0.2246 - val_loss: 3.7179 - val_mean_absolute_error: 1.3087 Epoch 566/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1353 - mean_absolute_error: 0.2176 - val_loss: 3.2986 - val_mean_absolute_error: 1.4036 Epoch 567/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1588 - mean_absolute_error: 0.2072 - val_loss: 3.3621 - val_mean_absolute_error: 1.3303 Epoch 568/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5106 - mean_absolute_error: 0.3287 - val_loss: 3.6060 - val_mean_absolute_error: 1.4037 Epoch 569/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.4405 - mean_absolute_error: 0.4029 - val_loss: 3.4911 - val_mean_absolute_error: 1.5200 Epoch 570/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2994 - mean_absolute_error: 0.3948 - val_loss: 3.1614 - val_mean_absolute_error: 1.3033 Epoch 571/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.2228 - mean_absolute_error: 0.3306 - val_loss: 3.5117 - val_mean_absolute_error: 1.3620 Epoch 572/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1848 - mean_absolute_error: 0.2934 - val_loss: 3.1472 - val_mean_absolute_error: 1.3044 Epoch 573/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1696 - mean_absolute_error: 0.2751 - val_loss: 3.3050 - val_mean_absolute_error: 1.3925 Epoch 574/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1403 - mean_absolute_error: 0.2552 - val_loss: 3.2587 - val_mean_absolute_error: 1.3527 Epoch 575/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1479 - mean_absolute_error: 0.2493 - val_loss: 3.3158 - val_mean_absolute_error: 1.3808 Epoch 576/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.1921 - mean_absolute_error: 0.2597 - val_loss: 3.5943 - val_mean_absolute_error: 1.3792 Epoch 577/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1936 - mean_absolute_error: 0.2525 - val_loss: 3.3794 - val_mean_absolute_error: 1.4251 Epoch 578/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2339 - mean_absolute_error: 0.2694 - val_loss: 3.5526 - val_mean_absolute_error: 1.3307 Epoch 579/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1970 - mean_absolute_error: 0.2899 - val_loss: 3.5056 - val_mean_absolute_error: 1.4868 Epoch 580/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5033 - mean_absolute_error: 0.4149 - val_loss: 3.8747 - val_mean_absolute_error: 1.4704 Epoch 581/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5737 - mean_absolute_error: 0.5107 - val_loss: 3.9419 - val_mean_absolute_error: 1.6339 Epoch 582/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.4885 - mean_absolute_error: 0.5077 - val_loss: 3.3599 - val_mean_absolute_error: 1.3219 Epoch 583/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3989 - mean_absolute_error: 0.4675 - val_loss: 3.2321 - val_mean_absolute_error: 1.4110 Epoch 584/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3980 - mean_absolute_error: 0.4474 - val_loss: 3.3459 - val_mean_absolute_error: 1.3410 Epoch 585/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3016 - mean_absolute_error: 0.3800 - val_loss: 3.4805 - val_mean_absolute_error: 1.4200 Epoch 586/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2393 - mean_absolute_error: 0.3482 - val_loss: 3.1327 - val_mean_absolute_error: 1.3392 Epoch 587/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2509 - mean_absolute_error: 0.3284 - val_loss: 3.2317 - val_mean_absolute_error: 1.4010 Epoch 588/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3550 - mean_absolute_error: 0.3684 - val_loss: 3.6554 - val_mean_absolute_error: 1.3597 Epoch 589/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3709 - mean_absolute_error: 0.3563 - val_loss: 3.4160 - val_mean_absolute_error: 1.4250 Epoch 590/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.3285 - mean_absolute_error: 0.3660 - val_loss: 3.1268 - val_mean_absolute_error: 1.3763 Epoch 591/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2964 - mean_absolute_error: 0.3436 - val_loss: 3.6420 - val_mean_absolute_error: 1.4358 Epoch 592/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2612 - mean_absolute_error: 0.3552 - val_loss: 3.5723 - val_mean_absolute_error: 1.3237 Epoch 593/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1828 - mean_absolute_error: 0.2942 - val_loss: 3.4597 - val_mean_absolute_error: 1.4556 Epoch 594/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1698 - mean_absolute_error: 0.2738 - val_loss: 3.4346 - val_mean_absolute_error: 1.3811 Epoch 595/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1340 - mean_absolute_error: 0.2245 - val_loss: 3.5659 - val_mean_absolute_error: 1.4915 Epoch 596/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4244 - mean_absolute_error: 0.3664 - val_loss: 3.9644 - val_mean_absolute_error: 1.4077 Epoch 597/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.3757 - mean_absolute_error: 0.3886 - val_loss: 3.4832 - val_mean_absolute_error: 1.5664 Epoch 598/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.3949 - mean_absolute_error: 0.4636 - val_loss: 3.3320 - val_mean_absolute_error: 1.2037 Epoch 599/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.3863 - mean_absolute_error: 0.4542 - val_loss: 3.9879 - val_mean_absolute_error: 1.4081 Epoch 600/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5023 - mean_absolute_error: 0.4993 - val_loss: 3.0238 - val_mean_absolute_error: 1.3780 Epoch 601/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.5355 - mean_absolute_error: 0.4856 - val_loss: 3.0625 - val_mean_absolute_error: 1.3123 Epoch 602/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3496 - mean_absolute_error: 0.4230 - val_loss: 3.2361 - val_mean_absolute_error: 1.3708 Epoch 603/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4652 - mean_absolute_error: 0.4457 - val_loss: 4.4412 - val_mean_absolute_error: 1.5745 Epoch 604/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.5374 - mean_absolute_error: 0.5194 - val_loss: 3.2979 - val_mean_absolute_error: 1.4116 Epoch 605/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4638 - mean_absolute_error: 0.4961 - val_loss: 3.5883 - val_mean_absolute_error: 1.4576 Epoch 606/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5845 - mean_absolute_error: 0.5025 - val_loss: 3.4923 - val_mean_absolute_error: 1.4220 Epoch 607/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.5890 - mean_absolute_error: 0.5982 - val_loss: 2.8728 - val_mean_absolute_error: 1.3913 Epoch 608/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5732 - mean_absolute_error: 0.5923 - val_loss: 4.1742 - val_mean_absolute_error: 1.4598 Epoch 609/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.4835 - mean_absolute_error: 0.5153 - val_loss: 2.6225 - val_mean_absolute_error: 1.2187 Epoch 610/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5169 - mean_absolute_error: 0.5521 - val_loss: 3.3965 - val_mean_absolute_error: 1.3185 Epoch 611/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4514 - mean_absolute_error: 0.4976 - val_loss: 3.7197 - val_mean_absolute_error: 1.5295 Epoch 612/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.4388 - mean_absolute_error: 0.4900 - val_loss: 3.2193 - val_mean_absolute_error: 1.3680 Epoch 613/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.3970 - mean_absolute_error: 0.4768 - val_loss: 3.7503 - val_mean_absolute_error: 1.4776 Epoch 614/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.3346 - mean_absolute_error: 0.4214 - val_loss: 3.0086 - val_mean_absolute_error: 1.2805 Epoch 615/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2567 - mean_absolute_error: 0.3484 - val_loss: 3.0572 - val_mean_absolute_error: 1.3429 Epoch 616/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1742 - mean_absolute_error: 0.2823 - val_loss: 3.4551 - val_mean_absolute_error: 1.3490 Epoch 617/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1222 - mean_absolute_error: 0.2262 - val_loss: 3.4274 - val_mean_absolute_error: 1.4016 Epoch 618/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1444 - mean_absolute_error: 0.1995 - val_loss: 3.3644 - val_mean_absolute_error: 1.3477 Epoch 619/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1268 - mean_absolute_error: 0.1921 - val_loss: 3.3262 - val_mean_absolute_error: 1.4142 Epoch 620/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1010 - mean_absolute_error: 0.1978 - val_loss: 3.3231 - val_mean_absolute_error: 1.3524 Epoch 621/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0907 - mean_absolute_error: 0.1660 - val_loss: 3.2394 - val_mean_absolute_error: 1.3439 Epoch 622/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.0911 - mean_absolute_error: 0.1692 - val_loss: 3.4426 - val_mean_absolute_error: 1.3577 Epoch 623/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.1354 - mean_absolute_error: 0.2006 - val_loss: 3.1838 - val_mean_absolute_error: 1.3658 Epoch 624/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.1283 - mean_absolute_error: 0.2251 - val_loss: 3.5653 - val_mean_absolute_error: 1.3488 Epoch 625/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.1107 - mean_absolute_error: 0.2193 - val_loss: 3.3237 - val_mean_absolute_error: 1.3895 Epoch 626/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.1415 - mean_absolute_error: 0.2326 - val_loss: 3.2274 - val_mean_absolute_error: 1.3648 Epoch 627/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.1074 - mean_absolute_error: 0.1998 - val_loss: 3.3602 - val_mean_absolute_error: 1.3690 Epoch 628/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.1028 - mean_absolute_error: 0.1627 - val_loss: 3.3060 - val_mean_absolute_error: 1.3677 Epoch 629/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.1184 - mean_absolute_error: 0.1752 - val_loss: 3.2306 - val_mean_absolute_error: 1.3607 Epoch 630/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.0981 - mean_absolute_error: 0.1681 - val_loss: 3.2720 - val_mean_absolute_error: 1.3091 Epoch 631/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0869 - mean_absolute_error: 0.1670 - val_loss: 3.4016 - val_mean_absolute_error: 1.3863 Epoch 632/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1850 - mean_absolute_error: 0.2047 - val_loss: 3.3266 - val_mean_absolute_error: 1.3645 Epoch 633/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1345 - mean_absolute_error: 0.1703 - val_loss: 3.4918 - val_mean_absolute_error: 1.4070 Epoch 634/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1280 - mean_absolute_error: 0.2089 - val_loss: 3.2649 - val_mean_absolute_error: 1.3232 Epoch 635/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1185 - mean_absolute_error: 0.2086 - val_loss: 3.4599 - val_mean_absolute_error: 1.3534 Epoch 636/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.0859 - mean_absolute_error: 0.2120 - val_loss: 3.4855 - val_mean_absolute_error: 1.3249 Epoch 637/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1530 - mean_absolute_error: 0.2146 - val_loss: 3.2503 - val_mean_absolute_error: 1.3893 Epoch 638/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1356 - mean_absolute_error: 0.2488 - val_loss: 3.6138 - val_mean_absolute_error: 1.4166 Epoch 639/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1558 - mean_absolute_error: 0.2253 - val_loss: 3.7453 - val_mean_absolute_error: 1.3581 Epoch 640/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2032 - mean_absolute_error: 0.2542 - val_loss: 3.3424 - val_mean_absolute_error: 1.4697 Epoch 641/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.8019 - mean_absolute_error: 0.4995 - val_loss: 4.1303 - val_mean_absolute_error: 1.5337 Epoch 642/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.6292 - mean_absolute_error: 0.4473 - val_loss: 3.8727 - val_mean_absolute_error: 1.6337 Epoch 643/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.7157 - mean_absolute_error: 0.5344 - val_loss: 3.3982 - val_mean_absolute_error: 1.4301 Epoch 644/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3756 - mean_absolute_error: 0.4274 - val_loss: 3.2630 - val_mean_absolute_error: 1.3590 Epoch 645/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4955 - mean_absolute_error: 0.4320 - val_loss: 3.8629 - val_mean_absolute_error: 1.3556 Epoch 646/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4902 - mean_absolute_error: 0.4505 - val_loss: 3.6552 - val_mean_absolute_error: 1.5765 Epoch 647/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3718 - mean_absolute_error: 0.4372 - val_loss: 3.6509 - val_mean_absolute_error: 1.4045 Epoch 648/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.3507 - mean_absolute_error: 0.4066 - val_loss: 3.7606 - val_mean_absolute_error: 1.5469 Epoch 649/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2186 - mean_absolute_error: 0.3384 - val_loss: 3.3785 - val_mean_absolute_error: 1.3792 Epoch 650/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1944 - mean_absolute_error: 0.3062 - val_loss: 3.2753 - val_mean_absolute_error: 1.4544 Epoch 651/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1424 - mean_absolute_error: 0.2632 - val_loss: 3.5414 - val_mean_absolute_error: 1.3947 Epoch 652/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1202 - mean_absolute_error: 0.2270 - val_loss: 3.3666 - val_mean_absolute_error: 1.4152 Epoch 653/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0921 - mean_absolute_error: 0.1651 - val_loss: 3.4395 - val_mean_absolute_error: 1.3685 Epoch 654/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0814 - mean_absolute_error: 0.1467 - val_loss: 3.3231 - val_mean_absolute_error: 1.4288 Epoch 655/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0805 - mean_absolute_error: 0.1485 - val_loss: 3.3958 - val_mean_absolute_error: 1.3964 Epoch 656/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0941 - mean_absolute_error: 0.1387 - val_loss: 3.4481 - val_mean_absolute_error: 1.3934 Epoch 657/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0575 - mean_absolute_error: 0.1240 - val_loss: 3.5106 - val_mean_absolute_error: 1.4130 Epoch 658/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1049 - mean_absolute_error: 0.1463 - val_loss: 3.3500 - val_mean_absolute_error: 1.4291 Epoch 659/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2933 - mean_absolute_error: 0.3241 - val_loss: 3.7543 - val_mean_absolute_error: 1.4166 Epoch 660/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4827 - mean_absolute_error: 0.3995 - val_loss: 3.6131 - val_mean_absolute_error: 1.5173 Epoch 661/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4172 - mean_absolute_error: 0.4149 - val_loss: 3.1888 - val_mean_absolute_error: 1.4244 Epoch 662/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2691 - mean_absolute_error: 0.3531 - val_loss: 3.4749 - val_mean_absolute_error: 1.3180 Epoch 663/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2495 - mean_absolute_error: 0.3506 - val_loss: 3.1539 - val_mean_absolute_error: 1.3428 Epoch 664/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2029 - mean_absolute_error: 0.2977 - val_loss: 3.4995 - val_mean_absolute_error: 1.4132 Epoch 665/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1833 - mean_absolute_error: 0.2959 - val_loss: 3.6055 - val_mean_absolute_error: 1.4312 Epoch 666/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1661 - mean_absolute_error: 0.2636 - val_loss: 3.3535 - val_mean_absolute_error: 1.3792 Epoch 667/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1442 - mean_absolute_error: 0.2250 - val_loss: 3.2175 - val_mean_absolute_error: 1.3780 Epoch 668/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0985 - mean_absolute_error: 0.1998 - val_loss: 3.1494 - val_mean_absolute_error: 1.3178 Epoch 669/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.1141 - mean_absolute_error: 0.2001 - val_loss: 3.4019 - val_mean_absolute_error: 1.4340 Epoch 670/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.1004 - mean_absolute_error: 0.1822 - val_loss: 3.4945 - val_mean_absolute_error: 1.3559 Epoch 671/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.0876 - mean_absolute_error: 0.1850 - val_loss: 3.3864 - val_mean_absolute_error: 1.3901 Epoch 672/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.1204 - mean_absolute_error: 0.1929 - val_loss: 3.4271 - val_mean_absolute_error: 1.3620 Epoch 673/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1078 - mean_absolute_error: 0.1996 - val_loss: 3.1479 - val_mean_absolute_error: 1.3659 Epoch 674/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2380 - mean_absolute_error: 0.2447 - val_loss: 3.6526 - val_mean_absolute_error: 1.4147 Epoch 675/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1633 - mean_absolute_error: 0.2814 - val_loss: 3.3327 - val_mean_absolute_error: 1.3704 Epoch 676/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1328 - mean_absolute_error: 0.2357 - val_loss: 3.7247 - val_mean_absolute_error: 1.4248 Epoch 677/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.1805 - mean_absolute_error: 0.2566 - val_loss: 3.3603 - val_mean_absolute_error: 1.4188 Epoch 678/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1743 - mean_absolute_error: 0.2370 - val_loss: 3.3908 - val_mean_absolute_error: 1.3551 Epoch 679/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1966 - mean_absolute_error: 0.2565 - val_loss: 3.3000 - val_mean_absolute_error: 1.4437 Epoch 680/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1176 - mean_absolute_error: 0.2358 - val_loss: 3.7472 - val_mean_absolute_error: 1.4600 Epoch 681/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1147 - mean_absolute_error: 0.2242 - val_loss: 3.4427 - val_mean_absolute_error: 1.3996 Epoch 682/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1027 - mean_absolute_error: 0.2037 - val_loss: 3.2489 - val_mean_absolute_error: 1.3785 Epoch 683/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1522 - mean_absolute_error: 0.2248 - val_loss: 3.6368 - val_mean_absolute_error: 1.4051 Epoch 684/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1251 - mean_absolute_error: 0.2392 - val_loss: 2.9815 - val_mean_absolute_error: 1.3332 Epoch 685/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.2824 - mean_absolute_error: 0.3065 - val_loss: 3.7133 - val_mean_absolute_error: 1.4190 Epoch 686/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1752 - mean_absolute_error: 0.2746 - val_loss: 3.2354 - val_mean_absolute_error: 1.4103 Epoch 687/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2043 - mean_absolute_error: 0.3021 - val_loss: 3.4390 - val_mean_absolute_error: 1.3958 Epoch 688/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1647 - mean_absolute_error: 0.2710 - val_loss: 3.0355 - val_mean_absolute_error: 1.3219 Epoch 689/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2287 - mean_absolute_error: 0.3144 - val_loss: 3.0986 - val_mean_absolute_error: 1.4079 Epoch 690/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1360 - mean_absolute_error: 0.2577 - val_loss: 3.5288 - val_mean_absolute_error: 1.3817 Epoch 691/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1131 - mean_absolute_error: 0.2256 - val_loss: 3.3555 - val_mean_absolute_error: 1.4345 Epoch 692/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1005 - mean_absolute_error: 0.2257 - val_loss: 3.4560 - val_mean_absolute_error: 1.4049 Epoch 693/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0940 - mean_absolute_error: 0.1968 - val_loss: 3.2374 - val_mean_absolute_error: 1.4369 Epoch 694/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.0978 - mean_absolute_error: 0.1777 - val_loss: 3.3431 - val_mean_absolute_error: 1.3511 Epoch 695/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.1065 - mean_absolute_error: 0.1746 - val_loss: 3.3402 - val_mean_absolute_error: 1.4332 Epoch 696/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.0823 - mean_absolute_error: 0.1546 - val_loss: 3.4828 - val_mean_absolute_error: 1.3344 Epoch 697/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.0672 - mean_absolute_error: 0.1456 - val_loss: 3.3973 - val_mean_absolute_error: 1.4462 Epoch 698/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.0976 - mean_absolute_error: 0.1509 - val_loss: 3.3396 - val_mean_absolute_error: 1.3839 Epoch 699/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.0937 - mean_absolute_error: 0.1659 - val_loss: 3.4587 - val_mean_absolute_error: 1.4493 Epoch 700/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.1123 - mean_absolute_error: 0.1873 - val_loss: 3.4025 - val_mean_absolute_error: 1.3526 Epoch 701/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.0733 - mean_absolute_error: 0.1684 - val_loss: 3.3406 - val_mean_absolute_error: 1.4507 Epoch 702/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0853 - mean_absolute_error: 0.1714 - val_loss: 3.4107 - val_mean_absolute_error: 1.3604 Epoch 703/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0718 - mean_absolute_error: 0.1647 - val_loss: 3.5001 - val_mean_absolute_error: 1.4366 Epoch 704/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0578 - mean_absolute_error: 0.1457 - val_loss: 3.4143 - val_mean_absolute_error: 1.3706 Epoch 705/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0573 - mean_absolute_error: 0.1303 - val_loss: 3.2763 - val_mean_absolute_error: 1.4206 Epoch 706/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0876 - mean_absolute_error: 0.1549 - val_loss: 3.5021 - val_mean_absolute_error: 1.3993 Epoch 707/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0495 - mean_absolute_error: 0.1378 - val_loss: 3.4076 - val_mean_absolute_error: 1.4314 Epoch 708/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0784 - mean_absolute_error: 0.1576 - val_loss: 3.3441 - val_mean_absolute_error: 1.3920 Epoch 709/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0616 - mean_absolute_error: 0.1412 - val_loss: 3.3888 - val_mean_absolute_error: 1.4372 Epoch 710/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0503 - mean_absolute_error: 0.1302 - val_loss: 3.3840 - val_mean_absolute_error: 1.3726 Epoch 711/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.0932 - mean_absolute_error: 0.1634 - val_loss: 3.4313 - val_mean_absolute_error: 1.4149 Epoch 712/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1875 - mean_absolute_error: 0.2699 - val_loss: 3.5368 - val_mean_absolute_error: 1.4340 Epoch 713/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.3421 - mean_absolute_error: 0.3091 - val_loss: 3.7654 - val_mean_absolute_error: 1.5612 Epoch 714/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.2826 - mean_absolute_error: 0.3114 - val_loss: 3.4875 - val_mean_absolute_error: 1.3900 Epoch 715/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3337 - mean_absolute_error: 0.3745 - val_loss: 3.2865 - val_mean_absolute_error: 1.4344 Epoch 716/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2577 - mean_absolute_error: 0.3666 - val_loss: 3.5195 - val_mean_absolute_error: 1.2825 Epoch 717/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2307 - mean_absolute_error: 0.3535 - val_loss: 3.2804 - val_mean_absolute_error: 1.4325 Epoch 718/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1914 - mean_absolute_error: 0.3246 - val_loss: 3.5666 - val_mean_absolute_error: 1.4267 Epoch 719/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1757 - mean_absolute_error: 0.2979 - val_loss: 3.4307 - val_mean_absolute_error: 1.4308 Epoch 720/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1758 - mean_absolute_error: 0.2959 - val_loss: 3.1991 - val_mean_absolute_error: 1.3823 Epoch 721/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1367 - mean_absolute_error: 0.2498 - val_loss: 3.2550 - val_mean_absolute_error: 1.3721 Epoch 722/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1314 - mean_absolute_error: 0.2406 - val_loss: 3.0511 - val_mean_absolute_error: 1.3002 Epoch 723/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2237 - mean_absolute_error: 0.2570 - val_loss: 3.2851 - val_mean_absolute_error: 1.3837 Epoch 724/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1055 - mean_absolute_error: 0.2540 - val_loss: 3.3565 - val_mean_absolute_error: 1.4099 Epoch 725/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1132 - mean_absolute_error: 0.2189 - val_loss: 3.2252 - val_mean_absolute_error: 1.3280 Epoch 726/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1220 - mean_absolute_error: 0.2085 - val_loss: 3.2110 - val_mean_absolute_error: 1.3327 Epoch 727/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0735 - mean_absolute_error: 0.1763 - val_loss: 3.2814 - val_mean_absolute_error: 1.3609 Epoch 728/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0669 - mean_absolute_error: 0.1604 - val_loss: 3.4834 - val_mean_absolute_error: 1.4147 Epoch 729/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0878 - mean_absolute_error: 0.1850 - val_loss: 3.3066 - val_mean_absolute_error: 1.3186 Epoch 730/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0732 - mean_absolute_error: 0.1633 - val_loss: 3.4093 - val_mean_absolute_error: 1.4168 Epoch 731/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0610 - mean_absolute_error: 0.1511 - val_loss: 3.2538 - val_mean_absolute_error: 1.3557 Epoch 732/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.0442 - mean_absolute_error: 0.1373 - val_loss: 3.1860 - val_mean_absolute_error: 1.3742 Epoch 733/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0639 - mean_absolute_error: 0.1364 - val_loss: 3.4212 - val_mean_absolute_error: 1.3410 Epoch 734/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1126 - mean_absolute_error: 0.1560 - val_loss: 3.3468 - val_mean_absolute_error: 1.4075 Epoch 735/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0605 - mean_absolute_error: 0.1579 - val_loss: 3.2782 - val_mean_absolute_error: 1.3739 Epoch 736/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1051 - mean_absolute_error: 0.1823 - val_loss: 3.6057 - val_mean_absolute_error: 1.4162 Epoch 737/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0994 - mean_absolute_error: 0.1845 - val_loss: 3.3593 - val_mean_absolute_error: 1.4306 Epoch 738/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0657 - mean_absolute_error: 0.1678 - val_loss: 3.5414 - val_mean_absolute_error: 1.3767 Epoch 739/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1115 - mean_absolute_error: 0.1938 - val_loss: 3.4122 - val_mean_absolute_error: 1.4419 Epoch 740/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1107 - mean_absolute_error: 0.1960 - val_loss: 3.4048 - val_mean_absolute_error: 1.3392 Epoch 741/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0675 - mean_absolute_error: 0.1872 - val_loss: 3.3296 - val_mean_absolute_error: 1.4342 Epoch 742/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0932 - mean_absolute_error: 0.1775 - val_loss: 3.3693 - val_mean_absolute_error: 1.3249 Epoch 743/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0788 - mean_absolute_error: 0.1905 - val_loss: 3.4556 - val_mean_absolute_error: 1.5205 Epoch 744/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0981 - mean_absolute_error: 0.2164 - val_loss: 3.4908 - val_mean_absolute_error: 1.3029 Epoch 745/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1089 - mean_absolute_error: 0.2071 - val_loss: 3.2958 - val_mean_absolute_error: 1.3750 Epoch 746/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1226 - mean_absolute_error: 0.2049 - val_loss: 3.1207 - val_mean_absolute_error: 1.3412 Epoch 747/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1025 - mean_absolute_error: 0.2198 - val_loss: 3.2767 - val_mean_absolute_error: 1.3850 Epoch 748/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1052 - mean_absolute_error: 0.1897 - val_loss: 3.5115 - val_mean_absolute_error: 1.4520 Epoch 749/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1643 - mean_absolute_error: 0.2237 - val_loss: 3.2442 - val_mean_absolute_error: 1.3369 Epoch 750/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2668 - mean_absolute_error: 0.2603 - val_loss: 3.5772 - val_mean_absolute_error: 1.4497 Epoch 751/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1318 - mean_absolute_error: 0.2501 - val_loss: 3.3588 - val_mean_absolute_error: 1.4254 Epoch 752/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1138 - mean_absolute_error: 0.2384 - val_loss: 3.3676 - val_mean_absolute_error: 1.3584 Epoch 753/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1358 - mean_absolute_error: 0.2736 - val_loss: 3.5573 - val_mean_absolute_error: 1.4374 Epoch 754/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1577 - mean_absolute_error: 0.2816 - val_loss: 3.4275 - val_mean_absolute_error: 1.3330 Epoch 755/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1538 - mean_absolute_error: 0.2546 - val_loss: 3.4071 - val_mean_absolute_error: 1.4092 Epoch 756/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2877 - mean_absolute_error: 0.3684 - val_loss: 3.2809 - val_mean_absolute_error: 1.3057 Epoch 757/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2560 - mean_absolute_error: 0.3737 - val_loss: 3.2648 - val_mean_absolute_error: 1.4174 Epoch 758/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.2928 - mean_absolute_error: 0.3583 - val_loss: 3.6294 - val_mean_absolute_error: 1.4615 Epoch 759/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.3079 - mean_absolute_error: 0.4112 - val_loss: 3.3535 - val_mean_absolute_error: 1.3994 Epoch 760/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1663 - mean_absolute_error: 0.2889 - val_loss: 3.4975 - val_mean_absolute_error: 1.4045 Epoch 761/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.1832 - mean_absolute_error: 0.2958 - val_loss: 3.1774 - val_mean_absolute_error: 1.3989 Epoch 762/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1932 - mean_absolute_error: 0.2997 - val_loss: 3.4863 - val_mean_absolute_error: 1.3648 Epoch 763/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2434 - mean_absolute_error: 0.3414 - val_loss: 3.3872 - val_mean_absolute_error: 1.4505 Epoch 764/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3295 - mean_absolute_error: 0.3599 - val_loss: 3.4941 - val_mean_absolute_error: 1.3308 Epoch 765/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.6346 - mean_absolute_error: 0.4390 - val_loss: 3.9518 - val_mean_absolute_error: 1.6119 Epoch 766/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.9051 - mean_absolute_error: 0.5061 - val_loss: 3.7646 - val_mean_absolute_error: 1.4175 Epoch 767/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4286 - mean_absolute_error: 0.4692 - val_loss: 3.6122 - val_mean_absolute_error: 1.5754 Epoch 768/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.5159 - mean_absolute_error: 0.5390 - val_loss: 3.5115 - val_mean_absolute_error: 1.2957 Epoch 769/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3597 - mean_absolute_error: 0.4058 - val_loss: 3.7962 - val_mean_absolute_error: 1.5562 Epoch 770/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.5717 - mean_absolute_error: 0.4471 - val_loss: 3.6124 - val_mean_absolute_error: 1.3861 Epoch 771/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3569 - mean_absolute_error: 0.4327 - val_loss: 3.9025 - val_mean_absolute_error: 1.5233 Epoch 772/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2681 - mean_absolute_error: 0.3571 - val_loss: 3.3930 - val_mean_absolute_error: 1.3833 Epoch 773/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2508 - mean_absolute_error: 0.3922 - val_loss: 3.4541 - val_mean_absolute_error: 1.4986 Epoch 774/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2799 - mean_absolute_error: 0.3566 - val_loss: 3.8323 - val_mean_absolute_error: 1.4579 Epoch 775/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2423 - mean_absolute_error: 0.3825 - val_loss: 3.4487 - val_mean_absolute_error: 1.4297 Epoch 776/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.2157 - mean_absolute_error: 0.3420 - val_loss: 2.9573 - val_mean_absolute_error: 1.3247 Epoch 777/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1768 - mean_absolute_error: 0.3014 - val_loss: 3.6455 - val_mean_absolute_error: 1.4153 Epoch 778/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2062 - mean_absolute_error: 0.3212 - val_loss: 3.1450 - val_mean_absolute_error: 1.3165 Epoch 779/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1773 - mean_absolute_error: 0.3265 - val_loss: 3.3665 - val_mean_absolute_error: 1.4497 Epoch 780/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1733 - mean_absolute_error: 0.3083 - val_loss: 3.6120 - val_mean_absolute_error: 1.3699 Epoch 781/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.2052 - mean_absolute_error: 0.3128 - val_loss: 3.5079 - val_mean_absolute_error: 1.5051 Epoch 782/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3169 - mean_absolute_error: 0.3418 - val_loss: 3.4786 - val_mean_absolute_error: 1.3783 Epoch 783/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.3115 - mean_absolute_error: 0.3986 - val_loss: 3.8929 - val_mean_absolute_error: 1.6440 Epoch 784/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2955 - mean_absolute_error: 0.4128 - val_loss: 3.5344 - val_mean_absolute_error: 1.3339 Epoch 785/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2457 - mean_absolute_error: 0.3506 - val_loss: 3.1875 - val_mean_absolute_error: 1.5070 Epoch 786/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2031 - mean_absolute_error: 0.3123 - val_loss: 3.4421 - val_mean_absolute_error: 1.3658 Epoch 787/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1589 - mean_absolute_error: 0.2945 - val_loss: 3.5331 - val_mean_absolute_error: 1.4927 Epoch 788/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1338 - mean_absolute_error: 0.2630 - val_loss: 3.0752 - val_mean_absolute_error: 1.3329 Epoch 789/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1383 - mean_absolute_error: 0.2823 - val_loss: 3.2069 - val_mean_absolute_error: 1.3919 Epoch 790/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1416 - mean_absolute_error: 0.2366 - val_loss: 3.3179 - val_mean_absolute_error: 1.3719 Epoch 791/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2394 - mean_absolute_error: 0.3229 - val_loss: 3.4385 - val_mean_absolute_error: 1.4907 Epoch 792/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5823 - mean_absolute_error: 0.4084 - val_loss: 3.6397 - val_mean_absolute_error: 1.4616 Epoch 793/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5346 - mean_absolute_error: 0.3935 - val_loss: 3.7231 - val_mean_absolute_error: 1.6046 Epoch 794/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3377 - mean_absolute_error: 0.3449 - val_loss: 3.3338 - val_mean_absolute_error: 1.3554 Epoch 795/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3069 - mean_absolute_error: 0.3318 - val_loss: 3.5278 - val_mean_absolute_error: 1.4455 Epoch 796/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2804 - mean_absolute_error: 0.3474 - val_loss: 2.9664 - val_mean_absolute_error: 1.2770 Epoch 797/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2774 - mean_absolute_error: 0.3255 - val_loss: 3.1331 - val_mean_absolute_error: 1.3340 Epoch 798/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2214 - mean_absolute_error: 0.3365 - val_loss: 3.4855 - val_mean_absolute_error: 1.4662 Epoch 799/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2235 - mean_absolute_error: 0.3379 - val_loss: 3.1577 - val_mean_absolute_error: 1.3893 Epoch 800/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1726 - mean_absolute_error: 0.2933 - val_loss: 3.2809 - val_mean_absolute_error: 1.3906 Epoch 801/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1427 - mean_absolute_error: 0.2631 - val_loss: 3.3366 - val_mean_absolute_error: 1.4205 Epoch 802/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1816 - mean_absolute_error: 0.2966 - val_loss: 3.3844 - val_mean_absolute_error: 1.3808 Epoch 803/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1458 - mean_absolute_error: 0.2790 - val_loss: 2.9640 - val_mean_absolute_error: 1.4175 Epoch 804/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1598 - mean_absolute_error: 0.2924 - val_loss: 3.3643 - val_mean_absolute_error: 1.3578 Epoch 805/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1262 - mean_absolute_error: 0.2717 - val_loss: 3.5099 - val_mean_absolute_error: 1.4924 Epoch 806/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1283 - mean_absolute_error: 0.2770 - val_loss: 3.2034 - val_mean_absolute_error: 1.3444 Epoch 807/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1084 - mean_absolute_error: 0.2504 - val_loss: 3.6722 - val_mean_absolute_error: 1.4554 Epoch 808/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1351 - mean_absolute_error: 0.2587 - val_loss: 3.3197 - val_mean_absolute_error: 1.3852 Epoch 809/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1303 - mean_absolute_error: 0.2371 - val_loss: 3.2059 - val_mean_absolute_error: 1.3995 Epoch 810/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0937 - mean_absolute_error: 0.1851 - val_loss: 3.3015 - val_mean_absolute_error: 1.4080 Epoch 811/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0622 - mean_absolute_error: 0.1533 - val_loss: 3.2923 - val_mean_absolute_error: 1.3802 Epoch 812/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0527 - mean_absolute_error: 0.1442 - val_loss: 3.2640 - val_mean_absolute_error: 1.3983 Epoch 813/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0466 - mean_absolute_error: 0.1192 - val_loss: 3.3644 - val_mean_absolute_error: 1.4070 Epoch 814/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3200 - mean_absolute_error: 0.2358 - val_loss: 3.8180 - val_mean_absolute_error: 1.4956 Epoch 815/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3255 - mean_absolute_error: 0.2866 - val_loss: 3.3871 - val_mean_absolute_error: 1.4879 Epoch 816/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2583 - mean_absolute_error: 0.2676 - val_loss: 3.4413 - val_mean_absolute_error: 1.3879 Epoch 817/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1862 - mean_absolute_error: 0.2531 - val_loss: 3.4430 - val_mean_absolute_error: 1.3481 Epoch 818/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.1404 - mean_absolute_error: 0.2285 - val_loss: 3.2876 - val_mean_absolute_error: 1.3777 Epoch 819/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1150 - mean_absolute_error: 0.1934 - val_loss: 3.2798 - val_mean_absolute_error: 1.3675 Epoch 820/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1714 - mean_absolute_error: 0.1936 - val_loss: 3.2912 - val_mean_absolute_error: 1.4404 Epoch 821/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0865 - mean_absolute_error: 0.1973 - val_loss: 3.4526 - val_mean_absolute_error: 1.4011 Epoch 822/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0863 - mean_absolute_error: 0.1760 - val_loss: 3.2978 - val_mean_absolute_error: 1.4423 Epoch 823/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0607 - mean_absolute_error: 0.1539 - val_loss: 3.4005 - val_mean_absolute_error: 1.3729 Epoch 824/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0524 - mean_absolute_error: 0.1371 - val_loss: 3.3587 - val_mean_absolute_error: 1.4309 Epoch 825/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0722 - mean_absolute_error: 0.1423 - val_loss: 3.3119 - val_mean_absolute_error: 1.3702 Epoch 826/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1064 - mean_absolute_error: 0.1640 - val_loss: 3.4106 - val_mean_absolute_error: 1.4463 Epoch 827/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0835 - mean_absolute_error: 0.2100 - val_loss: 3.4616 - val_mean_absolute_error: 1.3800 Epoch 828/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.1720 - mean_absolute_error: 0.2173 - val_loss: 3.3714 - val_mean_absolute_error: 1.4410 Epoch 829/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.1722 - mean_absolute_error: 0.2172 - val_loss: 3.2239 - val_mean_absolute_error: 1.4161 Epoch 830/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1586 - mean_absolute_error: 0.2447 - val_loss: 3.2497 - val_mean_absolute_error: 1.3788 Epoch 831/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.1069 - mean_absolute_error: 0.2381 - val_loss: 3.4700 - val_mean_absolute_error: 1.4454 Epoch 832/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1147 - mean_absolute_error: 0.2445 - val_loss: 3.3436 - val_mean_absolute_error: 1.3182 Epoch 833/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1159 - mean_absolute_error: 0.2443 - val_loss: 3.1919 - val_mean_absolute_error: 1.4735 Epoch 834/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1376 - mean_absolute_error: 0.2411 - val_loss: 3.4327 - val_mean_absolute_error: 1.3324 Epoch 835/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1575 - mean_absolute_error: 0.2747 - val_loss: 3.4066 - val_mean_absolute_error: 1.4506 Epoch 836/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1316 - mean_absolute_error: 0.2826 - val_loss: 3.1624 - val_mean_absolute_error: 1.2880 Epoch 837/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1281 - mean_absolute_error: 0.2586 - val_loss: 3.3713 - val_mean_absolute_error: 1.4195 Epoch 838/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0702 - mean_absolute_error: 0.2030 - val_loss: 3.3721 - val_mean_absolute_error: 1.3531 Epoch 839/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0990 - mean_absolute_error: 0.1802 - val_loss: 3.1932 - val_mean_absolute_error: 1.4253 Epoch 840/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.0744 - mean_absolute_error: 0.1712 - val_loss: 3.4039 - val_mean_absolute_error: 1.4152 Epoch 841/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0693 - mean_absolute_error: 0.1759 - val_loss: 3.2726 - val_mean_absolute_error: 1.3255 Epoch 842/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.0784 - mean_absolute_error: 0.1931 - val_loss: 3.2846 - val_mean_absolute_error: 1.3917 Epoch 843/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.0936 - mean_absolute_error: 0.2092 - val_loss: 3.1582 - val_mean_absolute_error: 1.3359 Epoch 844/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.1023 - mean_absolute_error: 0.1966 - val_loss: 3.3507 - val_mean_absolute_error: 1.4557 Epoch 845/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.1344 - mean_absolute_error: 0.2032 - val_loss: 3.1959 - val_mean_absolute_error: 1.3719 Epoch 846/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.1179 - mean_absolute_error: 0.2264 - val_loss: 3.3773 - val_mean_absolute_error: 1.3686 Epoch 847/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1547 - mean_absolute_error: 0.2604 - val_loss: 3.4130 - val_mean_absolute_error: 1.4285 Epoch 848/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1807 - mean_absolute_error: 0.2908 - val_loss: 3.5585 - val_mean_absolute_error: 1.3976 Epoch 849/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.7540 - mean_absolute_error: 0.5584 - val_loss: 3.8985 - val_mean_absolute_error: 1.5715 Epoch 850/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.6416 - mean_absolute_error: 0.5538 - val_loss: 2.8567 - val_mean_absolute_error: 1.2853 Epoch 851/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.6155 - mean_absolute_error: 0.6180 - val_loss: 3.2027 - val_mean_absolute_error: 1.4052 Epoch 852/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.4945 - mean_absolute_error: 0.5053 - val_loss: 3.6788 - val_mean_absolute_error: 1.4251 Epoch 853/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.9051 - mean_absolute_error: 0.6646 - val_loss: 3.3608 - val_mean_absolute_error: 1.4899 Epoch 854/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.7677 - mean_absolute_error: 0.6141 - val_loss: 4.2233 - val_mean_absolute_error: 1.4472 Epoch 855/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.5869 - mean_absolute_error: 0.5962 - val_loss: 3.1306 - val_mean_absolute_error: 1.3321 Epoch 856/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.5523 - mean_absolute_error: 0.5612 - val_loss: 3.4050 - val_mean_absolute_error: 1.4905 Epoch 857/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.6388 - mean_absolute_error: 0.6218 - val_loss: 3.1652 - val_mean_absolute_error: 1.3330 Epoch 858/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5587 - mean_absolute_error: 0.5778 - val_loss: 3.3888 - val_mean_absolute_error: 1.3603 Epoch 859/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3322 - mean_absolute_error: 0.4446 - val_loss: 3.0320 - val_mean_absolute_error: 1.3798 Epoch 860/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3354 - mean_absolute_error: 0.4266 - val_loss: 3.7448 - val_mean_absolute_error: 1.4572 Epoch 861/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2302 - mean_absolute_error: 0.3698 - val_loss: 3.1911 - val_mean_absolute_error: 1.3418 Epoch 862/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2791 - mean_absolute_error: 0.3815 - val_loss: 3.6040 - val_mean_absolute_error: 1.4545 Epoch 863/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2402 - mean_absolute_error: 0.3772 - val_loss: 3.3521 - val_mean_absolute_error: 1.3213 Epoch 864/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1308 - mean_absolute_error: 0.2853 - val_loss: 3.1655 - val_mean_absolute_error: 1.3699 Epoch 865/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1929 - mean_absolute_error: 0.3116 - val_loss: 3.6820 - val_mean_absolute_error: 1.4110 Epoch 866/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1678 - mean_absolute_error: 0.3025 - val_loss: 3.0550 - val_mean_absolute_error: 1.4416 Epoch 867/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1072 - mean_absolute_error: 0.2578 - val_loss: 3.4922 - val_mean_absolute_error: 1.3768 Epoch 868/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1120 - mean_absolute_error: 0.2495 - val_loss: 3.0948 - val_mean_absolute_error: 1.4457 Epoch 869/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.0738 - mean_absolute_error: 0.1989 - val_loss: 3.4722 - val_mean_absolute_error: 1.3691 Epoch 870/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0898 - mean_absolute_error: 0.2131 - val_loss: 3.3030 - val_mean_absolute_error: 1.4533 Epoch 871/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0924 - mean_absolute_error: 0.2207 - val_loss: 3.5300 - val_mean_absolute_error: 1.3843 Epoch 872/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1152 - mean_absolute_error: 0.2387 - val_loss: 3.4986 - val_mean_absolute_error: 1.4419 Epoch 873/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1107 - mean_absolute_error: 0.2295 - val_loss: 3.2835 - val_mean_absolute_error: 1.3727 Epoch 874/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1197 - mean_absolute_error: 0.2324 - val_loss: 3.3058 - val_mean_absolute_error: 1.4070 Epoch 875/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.0833 - mean_absolute_error: 0.1986 - val_loss: 3.4095 - val_mean_absolute_error: 1.4294 Epoch 876/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1744 - mean_absolute_error: 0.2395 - val_loss: 3.4604 - val_mean_absolute_error: 1.3862 Epoch 877/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1071 - mean_absolute_error: 0.2499 - val_loss: 3.4416 - val_mean_absolute_error: 1.4396 Epoch 878/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0948 - mean_absolute_error: 0.2202 - val_loss: 3.4440 - val_mean_absolute_error: 1.4156 Epoch 879/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0852 - mean_absolute_error: 0.2287 - val_loss: 3.2672 - val_mean_absolute_error: 1.3833 Epoch 880/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0877 - mean_absolute_error: 0.2152 - val_loss: 3.5455 - val_mean_absolute_error: 1.4405 Epoch 881/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.0828 - mean_absolute_error: 0.2044 - val_loss: 3.3163 - val_mean_absolute_error: 1.4045 Epoch 882/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.0980 - mean_absolute_error: 0.2220 - val_loss: 3.4795 - val_mean_absolute_error: 1.4241 Epoch 883/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.0903 - mean_absolute_error: 0.2049 - val_loss: 3.2267 - val_mean_absolute_error: 1.3808 Epoch 884/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.0586 - mean_absolute_error: 0.1782 - val_loss: 3.2076 - val_mean_absolute_error: 1.3312 Epoch 885/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.3780 - mean_absolute_error: 0.2698 - val_loss: 3.5721 - val_mean_absolute_error: 1.4237 Epoch 886/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.2567 - mean_absolute_error: 0.2728 - val_loss: 3.0840 - val_mean_absolute_error: 1.3003 Epoch 887/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.2863 - mean_absolute_error: 0.2937 - val_loss: 3.3644 - val_mean_absolute_error: 1.3957 Epoch 888/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.2183 - mean_absolute_error: 0.2736 - val_loss: 3.3051 - val_mean_absolute_error: 1.4589 Epoch 889/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1568 - mean_absolute_error: 0.2502 - val_loss: 3.2041 - val_mean_absolute_error: 1.3353 Epoch 890/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0876 - mean_absolute_error: 0.2240 - val_loss: 3.4887 - val_mean_absolute_error: 1.3585 Epoch 891/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1368 - mean_absolute_error: 0.1965 - val_loss: 3.1454 - val_mean_absolute_error: 1.3592 Epoch 892/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1683 - mean_absolute_error: 0.2215 - val_loss: 3.2485 - val_mean_absolute_error: 1.4299 Epoch 893/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0651 - mean_absolute_error: 0.1761 - val_loss: 3.4829 - val_mean_absolute_error: 1.3868 Epoch 894/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0776 - mean_absolute_error: 0.1544 - val_loss: 3.3340 - val_mean_absolute_error: 1.3726 Epoch 895/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0899 - mean_absolute_error: 0.1597 - val_loss: 3.4394 - val_mean_absolute_error: 1.4114 Epoch 896/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0536 - mean_absolute_error: 0.1473 - val_loss: 3.2890 - val_mean_absolute_error: 1.4153 Epoch 897/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0326 - mean_absolute_error: 0.1240 - val_loss: 3.3551 - val_mean_absolute_error: 1.3767 Epoch 898/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0459 - mean_absolute_error: 0.1412 - val_loss: 3.4358 - val_mean_absolute_error: 1.4306 Epoch 899/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1258 - mean_absolute_error: 0.1864 - val_loss: 3.3944 - val_mean_absolute_error: 1.4127 Epoch 900/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1601 - mean_absolute_error: 0.1873 - val_loss: 3.2079 - val_mean_absolute_error: 1.3868 Epoch 901/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1616 - mean_absolute_error: 0.2333 - val_loss: 3.4340 - val_mean_absolute_error: 1.4074 Epoch 902/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0739 - mean_absolute_error: 0.1772 - val_loss: 3.4423 - val_mean_absolute_error: 1.4727 Epoch 903/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0521 - mean_absolute_error: 0.1610 - val_loss: 3.3384 - val_mean_absolute_error: 1.3471 Epoch 904/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0521 - mean_absolute_error: 0.1573 - val_loss: 3.4068 - val_mean_absolute_error: 1.3970 Epoch 905/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0409 - mean_absolute_error: 0.1599 - val_loss: 3.3435 - val_mean_absolute_error: 1.3302 Epoch 906/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0643 - mean_absolute_error: 0.1578 - val_loss: 3.2479 - val_mean_absolute_error: 1.3930 Epoch 907/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1820 - mean_absolute_error: 0.1742 - val_loss: 3.4338 - val_mean_absolute_error: 1.4335 Epoch 908/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.1030 - mean_absolute_error: 0.1966 - val_loss: 3.4274 - val_mean_absolute_error: 1.4130 Epoch 909/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0587 - mean_absolute_error: 0.1665 - val_loss: 3.4722 - val_mean_absolute_error: 1.4473 Epoch 910/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0482 - mean_absolute_error: 0.1557 - val_loss: 3.2392 - val_mean_absolute_error: 1.3515 Epoch 911/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0315 - mean_absolute_error: 0.1326 - val_loss: 3.3417 - val_mean_absolute_error: 1.4406 Epoch 912/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0230 - mean_absolute_error: 0.1113 - val_loss: 3.4077 - val_mean_absolute_error: 1.3573 Epoch 913/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0215 - mean_absolute_error: 0.0896 - val_loss: 3.3183 - val_mean_absolute_error: 1.4480 Epoch 914/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0149 - mean_absolute_error: 0.0818 - val_loss: 3.4104 - val_mean_absolute_error: 1.3603 Epoch 915/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0167 - mean_absolute_error: 0.0905 - val_loss: 3.3721 - val_mean_absolute_error: 1.4241 Epoch 916/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0142 - mean_absolute_error: 0.0695 - val_loss: 3.3919 - val_mean_absolute_error: 1.4049 Epoch 917/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0168 - mean_absolute_error: 0.0707 - val_loss: 3.3781 - val_mean_absolute_error: 1.4323 Epoch 918/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.0188 - mean_absolute_error: 0.0765 - val_loss: 3.3871 - val_mean_absolute_error: 1.3758 Epoch 919/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0118 - mean_absolute_error: 0.0733 - val_loss: 3.3872 - val_mean_absolute_error: 1.4375 Epoch 920/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0123 - mean_absolute_error: 0.0711 - val_loss: 3.3493 - val_mean_absolute_error: 1.4045 Epoch 921/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0220 - mean_absolute_error: 0.0804 - val_loss: 3.3731 - val_mean_absolute_error: 1.4225 Epoch 922/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0127 - mean_absolute_error: 0.0747 - val_loss: 3.3932 - val_mean_absolute_error: 1.3963 Epoch 923/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0123 - mean_absolute_error: 0.0767 - val_loss: 3.3119 - val_mean_absolute_error: 1.4203 Epoch 924/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0134 - mean_absolute_error: 0.0808 - val_loss: 3.3382 - val_mean_absolute_error: 1.3717 Epoch 925/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0091 - mean_absolute_error: 0.0680 - val_loss: 3.3871 - val_mean_absolute_error: 1.4249 Epoch 926/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0078 - mean_absolute_error: 0.0595 - val_loss: 3.3753 - val_mean_absolute_error: 1.3873 Epoch 927/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0066 - mean_absolute_error: 0.0519 - val_loss: 3.3763 - val_mean_absolute_error: 1.4361 Epoch 928/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0069 - mean_absolute_error: 0.0566 - val_loss: 3.4029 - val_mean_absolute_error: 1.3914 Epoch 929/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0069 - mean_absolute_error: 0.0542 - val_loss: 3.3659 - val_mean_absolute_error: 1.4101 Epoch 930/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0101 - mean_absolute_error: 0.0548 - val_loss: 3.3366 - val_mean_absolute_error: 1.4086 Epoch 931/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0083 - mean_absolute_error: 0.0570 - val_loss: 3.4155 - val_mean_absolute_error: 1.4180 Epoch 932/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0059 - mean_absolute_error: 0.0540 - val_loss: 3.4060 - val_mean_absolute_error: 1.4242 Epoch 933/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0053 - mean_absolute_error: 0.0479 - val_loss: 3.3854 - val_mean_absolute_error: 1.4032 Epoch 934/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0071 - mean_absolute_error: 0.0483 - val_loss: 3.3615 - val_mean_absolute_error: 1.4380 Epoch 935/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0246 - mean_absolute_error: 0.0780 - val_loss: 3.4734 - val_mean_absolute_error: 1.3825 Epoch 936/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0595 - mean_absolute_error: 0.1144 - val_loss: 3.3416 - val_mean_absolute_error: 1.4357 Epoch 937/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0895 - mean_absolute_error: 0.1373 - val_loss: 3.3891 - val_mean_absolute_error: 1.3950 Epoch 938/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0973 - mean_absolute_error: 0.1300 - val_loss: 3.4647 - val_mean_absolute_error: 1.4436 Epoch 939/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0562 - mean_absolute_error: 0.1594 - val_loss: 3.2954 - val_mean_absolute_error: 1.3933 Epoch 940/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0718 - mean_absolute_error: 0.1619 - val_loss: 3.6541 - val_mean_absolute_error: 1.4291 Epoch 941/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1425 - mean_absolute_error: 0.2188 - val_loss: 3.3452 - val_mean_absolute_error: 1.4324 Epoch 942/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1479 - mean_absolute_error: 0.2489 - val_loss: 3.2916 - val_mean_absolute_error: 1.3899 Epoch 943/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1190 - mean_absolute_error: 0.2440 - val_loss: 3.5228 - val_mean_absolute_error: 1.3764 Epoch 944/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.0658 - mean_absolute_error: 0.1978 - val_loss: 3.3873 - val_mean_absolute_error: 1.3853 Epoch 945/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0616 - mean_absolute_error: 0.1846 - val_loss: 3.2013 - val_mean_absolute_error: 1.4002 Epoch 946/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0705 - mean_absolute_error: 0.1939 - val_loss: 3.7294 - val_mean_absolute_error: 1.4706 Epoch 947/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0750 - mean_absolute_error: 0.1920 - val_loss: 3.2201 - val_mean_absolute_error: 1.3284 Epoch 948/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0863 - mean_absolute_error: 0.2057 - val_loss: 3.2779 - val_mean_absolute_error: 1.4541 Epoch 949/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.4658 - mean_absolute_error: 0.3487 - val_loss: 4.8475 - val_mean_absolute_error: 1.6392 Epoch 950/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.5888 - mean_absolute_error: 0.4135 - val_loss: 4.3309 - val_mean_absolute_error: 1.6593 Epoch 951/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4928 - mean_absolute_error: 0.4779 - val_loss: 3.2255 - val_mean_absolute_error: 1.3728 Epoch 952/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.9954 - mean_absolute_error: 0.6388 - val_loss: 3.9038 - val_mean_absolute_error: 1.4606 Epoch 953/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5406 - mean_absolute_error: 0.5210 - val_loss: 2.9882 - val_mean_absolute_error: 1.2346 Epoch 954/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5754 - mean_absolute_error: 0.5263 - val_loss: 3.3033 - val_mean_absolute_error: 1.3420 Epoch 955/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.0238 - mean_absolute_error: 0.6273 - val_loss: 4.0075 - val_mean_absolute_error: 1.5460 Epoch 956/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.9181 - mean_absolute_error: 0.6162 - val_loss: 3.1060 - val_mean_absolute_error: 1.3435 Epoch 957/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.1824 - mean_absolute_error: 0.6594 - val_loss: 3.4518 - val_mean_absolute_error: 1.4103 Epoch 958/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.7798 - mean_absolute_error: 0.6021 - val_loss: 2.6390 - val_mean_absolute_error: 1.2807 Epoch 959/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.7088 - mean_absolute_error: 0.5322 - val_loss: 3.5522 - val_mean_absolute_error: 1.4990 Epoch 960/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.6707 - mean_absolute_error: 0.5426 - val_loss: 3.1995 - val_mean_absolute_error: 1.3278 Epoch 961/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.6753 - mean_absolute_error: 0.5208 - val_loss: 2.6761 - val_mean_absolute_error: 1.3567 Epoch 962/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4454 - mean_absolute_error: 0.4806 - val_loss: 3.2311 - val_mean_absolute_error: 1.3117 Epoch 963/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4210 - mean_absolute_error: 0.4471 - val_loss: 2.8999 - val_mean_absolute_error: 1.3101 Epoch 964/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2786 - mean_absolute_error: 0.3474 - val_loss: 3.1709 - val_mean_absolute_error: 1.3794 Epoch 965/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2667 - mean_absolute_error: 0.3454 - val_loss: 2.8991 - val_mean_absolute_error: 1.3306 Epoch 966/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2326 - mean_absolute_error: 0.3007 - val_loss: 2.8820 - val_mean_absolute_error: 1.2846 Epoch 967/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2783 - mean_absolute_error: 0.3369 - val_loss: 2.7321 - val_mean_absolute_error: 1.2322 Epoch 968/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2635 - mean_absolute_error: 0.3017 - val_loss: 3.1345 - val_mean_absolute_error: 1.3162 Epoch 969/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1997 - mean_absolute_error: 0.2635 - val_loss: 2.8648 - val_mean_absolute_error: 1.2910 Epoch 970/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1773 - mean_absolute_error: 0.2559 - val_loss: 3.0663 - val_mean_absolute_error: 1.2924 Epoch 971/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1311 - mean_absolute_error: 0.2150 - val_loss: 2.9195 - val_mean_absolute_error: 1.3297 Epoch 972/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1011 - mean_absolute_error: 0.1777 - val_loss: 3.0992 - val_mean_absolute_error: 1.3496 Epoch 973/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.1133 - mean_absolute_error: 0.1717 - val_loss: 3.0232 - val_mean_absolute_error: 1.3566 Epoch 974/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1035 - mean_absolute_error: 0.1758 - val_loss: 3.1432 - val_mean_absolute_error: 1.3257 Epoch 975/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0785 - mean_absolute_error: 0.1427 - val_loss: 3.1139 - val_mean_absolute_error: 1.3403 Epoch 976/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0762 - mean_absolute_error: 0.1221 - val_loss: 3.0890 - val_mean_absolute_error: 1.3407 Epoch 977/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0850 - mean_absolute_error: 0.1352 - val_loss: 3.1377 - val_mean_absolute_error: 1.3688 Epoch 978/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0744 - mean_absolute_error: 0.1295 - val_loss: 3.1110 - val_mean_absolute_error: 1.3280 Epoch 979/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0729 - mean_absolute_error: 0.1258 - val_loss: 3.1838 - val_mean_absolute_error: 1.3568 Epoch 980/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0785 - mean_absolute_error: 0.1407 - val_loss: 3.0942 - val_mean_absolute_error: 1.3487 Epoch 981/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0786 - mean_absolute_error: 0.1400 - val_loss: 3.1059 - val_mean_absolute_error: 1.3282 Epoch 982/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0853 - mean_absolute_error: 0.1465 - val_loss: 3.2510 - val_mean_absolute_error: 1.3781 Epoch 983/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0742 - mean_absolute_error: 0.1607 - val_loss: 3.1160 - val_mean_absolute_error: 1.3480 Epoch 984/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0688 - mean_absolute_error: 0.1536 - val_loss: 3.2291 - val_mean_absolute_error: 1.3736 Epoch 985/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0768 - mean_absolute_error: 0.1530 - val_loss: 3.1709 - val_mean_absolute_error: 1.3761 Epoch 986/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0814 - mean_absolute_error: 0.1515 - val_loss: 3.2049 - val_mean_absolute_error: 1.3784 Epoch 987/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.0720 - mean_absolute_error: 0.1372 - val_loss: 3.2771 - val_mean_absolute_error: 1.3727 Epoch 988/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0702 - mean_absolute_error: 0.1439 - val_loss: 3.1808 - val_mean_absolute_error: 1.3608 Epoch 989/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.0617 - mean_absolute_error: 0.1309 - val_loss: 3.1763 - val_mean_absolute_error: 1.3491 Epoch 990/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.0735 - mean_absolute_error: 0.1284 - val_loss: 3.2107 - val_mean_absolute_error: 1.3794 Epoch 991/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.0509 - mean_absolute_error: 0.1204 - val_loss: 3.2211 - val_mean_absolute_error: 1.3451 Epoch 992/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.0673 - mean_absolute_error: 0.1241 - val_loss: 3.2432 - val_mean_absolute_error: 1.4029 Epoch 993/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.0488 - mean_absolute_error: 0.1149 - val_loss: 3.2763 - val_mean_absolute_error: 1.3269 Epoch 994/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.0518 - mean_absolute_error: 0.1210 - val_loss: 3.2600 - val_mean_absolute_error: 1.3970 Epoch 995/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0525 - mean_absolute_error: 0.1193 - val_loss: 3.2149 - val_mean_absolute_error: 1.3469 Epoch 996/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0440 - mean_absolute_error: 0.1184 - val_loss: 3.1814 - val_mean_absolute_error: 1.3793 Epoch 997/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0523 - mean_absolute_error: 0.1318 - val_loss: 3.3480 - val_mean_absolute_error: 1.3778 Epoch 998/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.0711 - mean_absolute_error: 0.1192 - val_loss: 3.3750 - val_mean_absolute_error: 1.3978 Epoch 999/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0999 - mean_absolute_error: 0.1588 - val_loss: 3.1819 - val_mean_absolute_error: 1.3655 Epoch 1000/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0611 - mean_absolute_error: 0.1591 - val_loss: 3.2781 - val_mean_absolute_error: 1.3418 7/7 [==============================] - 1s 5ms/step 1/1 [==============================] - 0s 20ms/step 1/1 [==============================] - 0s 21ms/step
# Calculate the RMSE
rmse_lstm = sqrt(mean_squared_error(y_test_DBK, test_predictions_DBK))
print('The RMSE value of LSTM model (DBK): {:.4f}'.format(rmse_lstm))
The RMSE value of LSTM model (DBK): 2.6075
# Plot Training Observations VS Training Predictions
figure(figsize=(20, 10), dpi=100)
plt.plot(dates_train_DBK, train_predictions_DBK)
plt.plot(dates_train_DBK, y_train_DBK)
plt.title('LSTM: Training Actual Returns/Training Predicted Returns (DBK)', fontsize=16)
plt.legend(['DBK Training Predictions', 'DBK Training Observations'])
# Plot Testing Observations VS Testing Predictions
figure(figsize=(20, 10), dpi=100)
plt.plot(dates_test_DBK, test_predictions_DBK)
plt.plot(dates_test_DBK, y_test_DBK)
plt.title('LSTM: Testing Actual Returns/Testing Predicted Returns (DBK)', fontsize=16)
plt.legend(['DBK Testing Predictions', 'DBK Testing Observations'])
# General Plot (Training, Validation & testing)
figure(figsize=(20, 10), dpi=100)
plt.plot(dates_train_DBK, train_predictions_DBK)
plt.plot(dates_train_DBK, y_train_DBK)
plt.plot(dates_val_DBK, val_predictions_DBK)
plt.plot(dates_val_DBK, y_val_DBK)
plt.plot(dates_test_DBK, test_predictions_DBK)
plt.plot(dates_test_DBK, y_test_DBK)
plt.title('LSTM: General forecasting plot (DBK)', fontsize=16)
plt.legend(['DBK Training Predictions',
'DBK Training Observations',
'DBK Validation Predictions',
'DBK Validation Observations',
'DBK Testing Predictions',
'DBK Testing Observations'])
<matplotlib.legend.Legend at 0x7fa8c6366460>
# transform a time series dataset into a supervised learning dataset (Input : Output)
def BAC_to_windowed_BAC(BAC_RET, first_date_str, last_date_str, n=3):
first_date = str_to_datetime(first_date_str)
last_date = str_to_datetime(last_date_str)
target_date = first_date
dates_BAC = []
X, Y = [], []
last_time = False
while True:
BAC_subset = BAC_RET.loc[:target_date].tail(n+1)
if len(BAC_subset) != n+1:
print(f'Error: Window of size {n} is too large for date {target_date}')
return
values = BAC_subset['Ret_BAC'].to_numpy()
x, y = values[:-1], values[-1]
dates_BAC.append(target_date)
X.append(x)
Y.append(y)
next_week = BAC_RET.loc[target_date:target_date+datetime.timedelta(days=7)]
next_datetime_str = str(next_week.head(2).tail(1).index.values[0])
next_date_str = next_datetime_str.split('T')[0]
year_month_day = next_date_str.split('-')
year, month, day = year_month_day
next_date = datetime.datetime(day=int(day), month=int(month), year=int(year))
if last_time:
break
target_date = next_date
if target_date == last_date:
last_time = True
ret_BAC = pd.DataFrame({})
ret_BAC['Target Date'] = dates_BAC
X = np.array(X)
for i in range(0, n):
X[:, i]
ret_BAC[f'Target-{n-i}'] = X[:, i]
ret_BAC['Target'] = Y
return ret_BAC
# Start day second time around: '2020-01-03'
windowed_BAC = BAC_to_windowed_BAC(BAC_RET,
'2020-01-03',
'2020-12-30',
n=3)
# Convert our new dataset into numpy arrays (to feed it directly into a tensorflow model)
def windowed_BAC_to_date_X_y(windowed_dataframe):
BAC_as_np = windowed_dataframe.to_numpy()
dates_BAC = BAC_as_np[:, 0]
middle_matrix_BAC = BAC_as_np[:, 1:-1]
X_BAC = middle_matrix_BAC.reshape((len(dates_BAC), middle_matrix_BAC.shape[1], 1))
Y_BAC = BAC_as_np[:, -1]
return dates_BAC, X_BAC.astype(np.float32), Y_BAC.astype(np.float32)
dates_BAC, X_BAC, y_BAC = windowed_BAC_to_date_X_y(windowed_BAC)
# Split the data into training, validation and testing partitions
q_85_BAC = int(len(dates_BAC) * .85)
q_95_BAC = int(len(dates_BAC) * .95)
dates_train_BAC, X_train_BAC, y_train_BAC = dates_BAC[:q_85_BAC], X_BAC[:q_85_BAC], y_BAC[:q_85_BAC]
dates_val_BAC, X_val_BAC, y_val_BAC = dates_BAC[q_85_BAC:q_95_BAC], X_BAC[q_85_BAC:q_95_BAC], y_BAC[q_85_BAC:q_95_BAC]
dates_test_BAC, X_test_BAC, y_test_BAC = dates_BAC[q_95_BAC:], X_BAC[q_95_BAC:], y_BAC[q_95_BAC:]
# Create & train the LSTM model
model_BAC = Sequential([layers.Input((3, 1)),
layers.LSTM(264),
layers.Dense(132, activation='relu'),
layers.Dense(132, activation='relu'),
layers.Dense(1)])
model_BAC.compile(loss='mse',
optimizer=Adam(learning_rate=0.001),
metrics=['mean_absolute_error'])
# Fitting the LSTM model
model_BAC.fit(X_train_BAC, y_train_BAC, validation_data=(X_val_BAC, y_val_BAC), epochs=1000)
# Forecasting
train_predictions_BAC = model_BAC.predict(X_train_BAC).flatten()
val_predictions_BAC = model_BAC.predict(X_val_BAC).flatten()
test_predictions_BAC = model_BAC.predict(X_test_BAC).flatten()
Epoch 1/1000 7/7 [==============================] - 3s 112ms/step - loss: 14.8813 - mean_absolute_error: 2.6239 - val_loss: 11.5781 - val_mean_absolute_error: 2.0382 Epoch 2/1000 7/7 [==============================] - 0s 27ms/step - loss: 14.6534 - mean_absolute_error: 2.6105 - val_loss: 11.5078 - val_mean_absolute_error: 2.0286 Epoch 3/1000 7/7 [==============================] - 0s 22ms/step - loss: 14.4296 - mean_absolute_error: 2.5985 - val_loss: 11.5756 - val_mean_absolute_error: 2.0346 Epoch 4/1000 7/7 [==============================] - 0s 28ms/step - loss: 14.2191 - mean_absolute_error: 2.5901 - val_loss: 11.6671 - val_mean_absolute_error: 2.0499 Epoch 5/1000 7/7 [==============================] - 0s 26ms/step - loss: 13.9118 - mean_absolute_error: 2.5715 - val_loss: 11.7387 - val_mean_absolute_error: 2.0607 Epoch 6/1000 7/7 [==============================] - 0s 25ms/step - loss: 13.8642 - mean_absolute_error: 2.5603 - val_loss: 11.8101 - val_mean_absolute_error: 2.0657 Epoch 7/1000 7/7 [==============================] - 0s 35ms/step - loss: 13.4273 - mean_absolute_error: 2.5315 - val_loss: 11.6730 - val_mean_absolute_error: 2.0615 Epoch 8/1000 7/7 [==============================] - 0s 28ms/step - loss: 13.2265 - mean_absolute_error: 2.5258 - val_loss: 11.7576 - val_mean_absolute_error: 2.0627 Epoch 9/1000 7/7 [==============================] - 0s 19ms/step - loss: 12.9080 - mean_absolute_error: 2.5118 - val_loss: 11.4892 - val_mean_absolute_error: 2.0149 Epoch 10/1000 7/7 [==============================] - 0s 19ms/step - loss: 12.5856 - mean_absolute_error: 2.4830 - val_loss: 11.4736 - val_mean_absolute_error: 2.0085 Epoch 11/1000 7/7 [==============================] - 0s 20ms/step - loss: 12.2060 - mean_absolute_error: 2.4365 - val_loss: 11.4389 - val_mean_absolute_error: 2.0537 Epoch 12/1000 7/7 [==============================] - 0s 18ms/step - loss: 11.8953 - mean_absolute_error: 2.4164 - val_loss: 11.3391 - val_mean_absolute_error: 2.0719 Epoch 13/1000 7/7 [==============================] - 0s 20ms/step - loss: 11.5447 - mean_absolute_error: 2.4005 - val_loss: 11.4233 - val_mean_absolute_error: 2.0950 Epoch 14/1000 7/7 [==============================] - 0s 19ms/step - loss: 11.4386 - mean_absolute_error: 2.4208 - val_loss: 12.0177 - val_mean_absolute_error: 2.1977 Epoch 15/1000 7/7 [==============================] - 0s 19ms/step - loss: 11.0429 - mean_absolute_error: 2.3897 - val_loss: 12.0790 - val_mean_absolute_error: 2.2140 Epoch 16/1000 7/7 [==============================] - 0s 19ms/step - loss: 11.2179 - mean_absolute_error: 2.3695 - val_loss: 12.0721 - val_mean_absolute_error: 2.2639 Epoch 17/1000 7/7 [==============================] - 0s 22ms/step - loss: 10.7122 - mean_absolute_error: 2.3275 - val_loss: 12.1177 - val_mean_absolute_error: 2.2553 Epoch 18/1000 7/7 [==============================] - 0s 21ms/step - loss: 10.2804 - mean_absolute_error: 2.3120 - val_loss: 12.6052 - val_mean_absolute_error: 2.3578 Epoch 19/1000 7/7 [==============================] - 0s 22ms/step - loss: 9.9589 - mean_absolute_error: 2.3078 - val_loss: 12.7466 - val_mean_absolute_error: 2.4293 Epoch 20/1000 7/7 [==============================] - 0s 23ms/step - loss: 9.9819 - mean_absolute_error: 2.3017 - val_loss: 12.6230 - val_mean_absolute_error: 2.4351 Epoch 21/1000 7/7 [==============================] - 0s 22ms/step - loss: 9.8971 - mean_absolute_error: 2.3255 - val_loss: 13.0911 - val_mean_absolute_error: 2.4848 Epoch 22/1000 7/7 [==============================] - 0s 25ms/step - loss: 9.6080 - mean_absolute_error: 2.2780 - val_loss: 13.0307 - val_mean_absolute_error: 2.4966 Epoch 23/1000 7/7 [==============================] - 0s 21ms/step - loss: 9.3405 - mean_absolute_error: 2.2707 - val_loss: 13.2750 - val_mean_absolute_error: 2.5559 Epoch 24/1000 7/7 [==============================] - 0s 32ms/step - loss: 9.8356 - mean_absolute_error: 2.3163 - val_loss: 12.7779 - val_mean_absolute_error: 2.4845 Epoch 25/1000 7/7 [==============================] - 0s 24ms/step - loss: 9.0594 - mean_absolute_error: 2.2088 - val_loss: 12.2023 - val_mean_absolute_error: 2.3686 Epoch 26/1000 7/7 [==============================] - 0s 25ms/step - loss: 9.6780 - mean_absolute_error: 2.3192 - val_loss: 12.4346 - val_mean_absolute_error: 2.3908 Epoch 27/1000 7/7 [==============================] - 0s 29ms/step - loss: 9.3994 - mean_absolute_error: 2.2923 - val_loss: 12.1354 - val_mean_absolute_error: 2.4508 Epoch 28/1000 7/7 [==============================] - 0s 19ms/step - loss: 8.8461 - mean_absolute_error: 2.2214 - val_loss: 13.0585 - val_mean_absolute_error: 2.5838 Epoch 29/1000 7/7 [==============================] - 0s 20ms/step - loss: 8.7222 - mean_absolute_error: 2.1987 - val_loss: 13.3275 - val_mean_absolute_error: 2.6238 Epoch 30/1000 7/7 [==============================] - 0s 18ms/step - loss: 8.6088 - mean_absolute_error: 2.1958 - val_loss: 13.1153 - val_mean_absolute_error: 2.5993 Epoch 31/1000 7/7 [==============================] - 0s 19ms/step - loss: 8.5868 - mean_absolute_error: 2.2198 - val_loss: 13.1791 - val_mean_absolute_error: 2.5613 Epoch 32/1000 7/7 [==============================] - 0s 19ms/step - loss: 8.3970 - mean_absolute_error: 2.1801 - val_loss: 13.3865 - val_mean_absolute_error: 2.6686 Epoch 33/1000 7/7 [==============================] - 0s 18ms/step - loss: 8.3322 - mean_absolute_error: 2.1922 - val_loss: 14.2853 - val_mean_absolute_error: 2.7578 Epoch 34/1000 7/7 [==============================] - 0s 19ms/step - loss: 8.5778 - mean_absolute_error: 2.2449 - val_loss: 14.3613 - val_mean_absolute_error: 2.7252 Epoch 35/1000 7/7 [==============================] - 0s 20ms/step - loss: 8.5648 - mean_absolute_error: 2.1448 - val_loss: 12.6172 - val_mean_absolute_error: 2.5391 Epoch 36/1000 7/7 [==============================] - 0s 20ms/step - loss: 8.6007 - mean_absolute_error: 2.2286 - val_loss: 12.9621 - val_mean_absolute_error: 2.5480 Epoch 37/1000 7/7 [==============================] - 0s 17ms/step - loss: 8.2745 - mean_absolute_error: 2.2074 - val_loss: 13.6414 - val_mean_absolute_error: 2.6717 Epoch 38/1000 7/7 [==============================] - 0s 19ms/step - loss: 8.9280 - mean_absolute_error: 2.2118 - val_loss: 13.9346 - val_mean_absolute_error: 2.7181 Epoch 39/1000 7/7 [==============================] - 0s 20ms/step - loss: 8.1288 - mean_absolute_error: 2.1669 - val_loss: 12.9274 - val_mean_absolute_error: 2.5728 Epoch 40/1000 7/7 [==============================] - 0s 21ms/step - loss: 8.3352 - mean_absolute_error: 2.1636 - val_loss: 13.7995 - val_mean_absolute_error: 2.6585 Epoch 41/1000 7/7 [==============================] - 0s 19ms/step - loss: 8.1495 - mean_absolute_error: 2.1782 - val_loss: 13.7854 - val_mean_absolute_error: 2.7026 Epoch 42/1000 7/7 [==============================] - 0s 20ms/step - loss: 7.7530 - mean_absolute_error: 2.1205 - val_loss: 12.8499 - val_mean_absolute_error: 2.6092 Epoch 43/1000 7/7 [==============================] - 0s 20ms/step - loss: 7.7149 - mean_absolute_error: 2.1191 - val_loss: 12.8244 - val_mean_absolute_error: 2.5987 Epoch 44/1000 7/7 [==============================] - 0s 20ms/step - loss: 7.5152 - mean_absolute_error: 2.0885 - val_loss: 13.4312 - val_mean_absolute_error: 2.6633 Epoch 45/1000 7/7 [==============================] - 0s 19ms/step - loss: 7.4450 - mean_absolute_error: 2.0592 - val_loss: 12.9214 - val_mean_absolute_error: 2.5876 Epoch 46/1000 7/7 [==============================] - 0s 19ms/step - loss: 7.5790 - mean_absolute_error: 2.0968 - val_loss: 13.8606 - val_mean_absolute_error: 2.6019 Epoch 47/1000 7/7 [==============================] - 0s 21ms/step - loss: 7.3632 - mean_absolute_error: 2.0529 - val_loss: 13.6520 - val_mean_absolute_error: 2.6548 Epoch 48/1000 7/7 [==============================] - 0s 19ms/step - loss: 7.2112 - mean_absolute_error: 2.0358 - val_loss: 13.7063 - val_mean_absolute_error: 2.6593 Epoch 49/1000 7/7 [==============================] - 0s 20ms/step - loss: 7.3133 - mean_absolute_error: 2.0316 - val_loss: 13.5666 - val_mean_absolute_error: 2.6369 Epoch 50/1000 7/7 [==============================] - 0s 18ms/step - loss: 7.5064 - mean_absolute_error: 2.0835 - val_loss: 13.6609 - val_mean_absolute_error: 2.6386 Epoch 51/1000 7/7 [==============================] - 0s 19ms/step - loss: 7.1798 - mean_absolute_error: 2.0579 - val_loss: 13.3694 - val_mean_absolute_error: 2.6484 Epoch 52/1000 7/7 [==============================] - 0s 25ms/step - loss: 6.9799 - mean_absolute_error: 2.0222 - val_loss: 13.2812 - val_mean_absolute_error: 2.6340 Epoch 53/1000 7/7 [==============================] - 0s 22ms/step - loss: 6.9118 - mean_absolute_error: 2.0250 - val_loss: 12.9059 - val_mean_absolute_error: 2.5691 Epoch 54/1000 7/7 [==============================] - 0s 21ms/step - loss: 7.1676 - mean_absolute_error: 2.0267 - val_loss: 13.5148 - val_mean_absolute_error: 2.6641 Epoch 55/1000 7/7 [==============================] - 0s 20ms/step - loss: 7.1956 - mean_absolute_error: 2.0657 - val_loss: 13.4750 - val_mean_absolute_error: 2.6564 Epoch 56/1000 7/7 [==============================] - 0s 21ms/step - loss: 6.9404 - mean_absolute_error: 2.0292 - val_loss: 12.4637 - val_mean_absolute_error: 2.5340 Epoch 57/1000 7/7 [==============================] - 0s 20ms/step - loss: 6.6481 - mean_absolute_error: 1.9787 - val_loss: 13.5392 - val_mean_absolute_error: 2.6591 Epoch 58/1000 7/7 [==============================] - 0s 20ms/step - loss: 6.7077 - mean_absolute_error: 1.9676 - val_loss: 13.3153 - val_mean_absolute_error: 2.6415 Epoch 59/1000 7/7 [==============================] - 0s 20ms/step - loss: 6.9432 - mean_absolute_error: 1.9795 - val_loss: 12.6939 - val_mean_absolute_error: 2.5570 Epoch 60/1000 7/7 [==============================] - 0s 21ms/step - loss: 6.4665 - mean_absolute_error: 1.9373 - val_loss: 12.5153 - val_mean_absolute_error: 2.5304 Epoch 61/1000 7/7 [==============================] - 0s 20ms/step - loss: 6.4330 - mean_absolute_error: 1.9413 - val_loss: 12.8138 - val_mean_absolute_error: 2.5774 Epoch 62/1000 7/7 [==============================] - 0s 19ms/step - loss: 6.3206 - mean_absolute_error: 1.9300 - val_loss: 12.9154 - val_mean_absolute_error: 2.5903 Epoch 63/1000 7/7 [==============================] - 0s 18ms/step - loss: 6.4127 - mean_absolute_error: 1.9279 - val_loss: 12.9949 - val_mean_absolute_error: 2.6018 Epoch 64/1000 7/7 [==============================] - 0s 19ms/step - loss: 6.3367 - mean_absolute_error: 1.9524 - val_loss: 12.8087 - val_mean_absolute_error: 2.5932 Epoch 65/1000 7/7 [==============================] - 0s 20ms/step - loss: 6.0177 - mean_absolute_error: 1.8647 - val_loss: 12.4370 - val_mean_absolute_error: 2.5424 Epoch 66/1000 7/7 [==============================] - 0s 20ms/step - loss: 6.0295 - mean_absolute_error: 1.8827 - val_loss: 13.0858 - val_mean_absolute_error: 2.5805 Epoch 67/1000 7/7 [==============================] - 0s 19ms/step - loss: 6.0200 - mean_absolute_error: 1.8924 - val_loss: 12.5223 - val_mean_absolute_error: 2.5599 Epoch 68/1000 7/7 [==============================] - 0s 19ms/step - loss: 6.2355 - mean_absolute_error: 1.9369 - val_loss: 12.5230 - val_mean_absolute_error: 2.5688 Epoch 69/1000 7/7 [==============================] - 0s 20ms/step - loss: 5.9939 - mean_absolute_error: 1.8999 - val_loss: 11.9174 - val_mean_absolute_error: 2.5036 Epoch 70/1000 7/7 [==============================] - 0s 18ms/step - loss: 6.2333 - mean_absolute_error: 1.9152 - val_loss: 12.5639 - val_mean_absolute_error: 2.5336 Epoch 71/1000 7/7 [==============================] - 0s 20ms/step - loss: 5.9608 - mean_absolute_error: 1.9058 - val_loss: 11.9151 - val_mean_absolute_error: 2.4753 Epoch 72/1000 7/7 [==============================] - 0s 20ms/step - loss: 5.7915 - mean_absolute_error: 1.8698 - val_loss: 12.1622 - val_mean_absolute_error: 2.4977 Epoch 73/1000 7/7 [==============================] - 0s 20ms/step - loss: 5.8869 - mean_absolute_error: 1.8727 - val_loss: 12.6293 - val_mean_absolute_error: 2.5824 Epoch 74/1000 7/7 [==============================] - 0s 19ms/step - loss: 5.6479 - mean_absolute_error: 1.8336 - val_loss: 12.4103 - val_mean_absolute_error: 2.5291 Epoch 75/1000 7/7 [==============================] - 0s 19ms/step - loss: 5.5389 - mean_absolute_error: 1.8019 - val_loss: 12.4918 - val_mean_absolute_error: 2.5490 Epoch 76/1000 7/7 [==============================] - 0s 19ms/step - loss: 5.5037 - mean_absolute_error: 1.7902 - val_loss: 12.2234 - val_mean_absolute_error: 2.5171 Epoch 77/1000 7/7 [==============================] - 0s 30ms/step - loss: 5.3361 - mean_absolute_error: 1.7709 - val_loss: 11.7449 - val_mean_absolute_error: 2.5298 Epoch 78/1000 7/7 [==============================] - 0s 32ms/step - loss: 5.6037 - mean_absolute_error: 1.7962 - val_loss: 11.9822 - val_mean_absolute_error: 2.5774 Epoch 79/1000 7/7 [==============================] - 0s 19ms/step - loss: 5.3602 - mean_absolute_error: 1.7742 - val_loss: 11.9361 - val_mean_absolute_error: 2.4989 Epoch 80/1000 7/7 [==============================] - 0s 20ms/step - loss: 5.2399 - mean_absolute_error: 1.7712 - val_loss: 11.6439 - val_mean_absolute_error: 2.5057 Epoch 81/1000 7/7 [==============================] - 0s 20ms/step - loss: 5.1381 - mean_absolute_error: 1.7544 - val_loss: 11.3065 - val_mean_absolute_error: 2.4537 Epoch 82/1000 7/7 [==============================] - 0s 21ms/step - loss: 5.0918 - mean_absolute_error: 1.7371 - val_loss: 11.4851 - val_mean_absolute_error: 2.4708 Epoch 83/1000 7/7 [==============================] - 0s 22ms/step - loss: 5.0986 - mean_absolute_error: 1.7476 - val_loss: 11.9352 - val_mean_absolute_error: 2.5383 Epoch 84/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.9459 - mean_absolute_error: 1.6753 - val_loss: 11.6300 - val_mean_absolute_error: 2.5563 Epoch 85/1000 7/7 [==============================] - 0s 20ms/step - loss: 4.8896 - mean_absolute_error: 1.7087 - val_loss: 10.7460 - val_mean_absolute_error: 2.4513 Epoch 86/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.8507 - mean_absolute_error: 1.7050 - val_loss: 11.0260 - val_mean_absolute_error: 2.4433 Epoch 87/1000 7/7 [==============================] - 0s 20ms/step - loss: 5.0051 - mean_absolute_error: 1.7133 - val_loss: 11.5852 - val_mean_absolute_error: 2.4998 Epoch 88/1000 7/7 [==============================] - 0s 18ms/step - loss: 5.4793 - mean_absolute_error: 1.8046 - val_loss: 12.5219 - val_mean_absolute_error: 2.5568 Epoch 89/1000 7/7 [==============================] - 0s 19ms/step - loss: 5.7628 - mean_absolute_error: 1.8615 - val_loss: 9.7364 - val_mean_absolute_error: 2.3440 Epoch 90/1000 7/7 [==============================] - 0s 19ms/step - loss: 5.5944 - mean_absolute_error: 1.8338 - val_loss: 11.8754 - val_mean_absolute_error: 2.5606 Epoch 91/1000 7/7 [==============================] - 0s 18ms/step - loss: 5.6178 - mean_absolute_error: 1.8196 - val_loss: 10.9497 - val_mean_absolute_error: 2.4340 Epoch 92/1000 7/7 [==============================] - 0s 20ms/step - loss: 5.5146 - mean_absolute_error: 1.8293 - val_loss: 10.6790 - val_mean_absolute_error: 2.4603 Epoch 93/1000 7/7 [==============================] - 0s 20ms/step - loss: 4.6105 - mean_absolute_error: 1.6473 - val_loss: 12.1407 - val_mean_absolute_error: 2.5807 Epoch 94/1000 7/7 [==============================] - 0s 20ms/step - loss: 5.1948 - mean_absolute_error: 1.7687 - val_loss: 11.6685 - val_mean_absolute_error: 2.5115 Epoch 95/1000 7/7 [==============================] - 0s 20ms/step - loss: 4.5866 - mean_absolute_error: 1.6849 - val_loss: 11.0601 - val_mean_absolute_error: 2.4803 Epoch 96/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.4828 - mean_absolute_error: 1.6494 - val_loss: 10.8231 - val_mean_absolute_error: 2.4823 Epoch 97/1000 7/7 [==============================] - 0s 20ms/step - loss: 4.3043 - mean_absolute_error: 1.6054 - val_loss: 10.7673 - val_mean_absolute_error: 2.4655 Epoch 98/1000 7/7 [==============================] - 0s 21ms/step - loss: 4.2071 - mean_absolute_error: 1.5734 - val_loss: 11.0957 - val_mean_absolute_error: 2.4978 Epoch 99/1000 7/7 [==============================] - 0s 20ms/step - loss: 4.1250 - mean_absolute_error: 1.5686 - val_loss: 11.7161 - val_mean_absolute_error: 2.5094 Epoch 100/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.1819 - mean_absolute_error: 1.5800 - val_loss: 11.5696 - val_mean_absolute_error: 2.4679 Epoch 101/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.4452 - mean_absolute_error: 1.6348 - val_loss: 11.4281 - val_mean_absolute_error: 2.5224 Epoch 102/1000 7/7 [==============================] - 0s 18ms/step - loss: 4.2608 - mean_absolute_error: 1.6072 - val_loss: 11.8305 - val_mean_absolute_error: 2.5927 Epoch 103/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.0654 - mean_absolute_error: 1.5851 - val_loss: 11.6524 - val_mean_absolute_error: 2.6053 Epoch 104/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.9834 - mean_absolute_error: 1.5630 - val_loss: 12.4531 - val_mean_absolute_error: 2.6173 Epoch 105/1000 7/7 [==============================] - 0s 20ms/step - loss: 3.8692 - mean_absolute_error: 1.5471 - val_loss: 11.3603 - val_mean_absolute_error: 2.5560 Epoch 106/1000 7/7 [==============================] - 0s 18ms/step - loss: 4.0440 - mean_absolute_error: 1.5686 - val_loss: 11.9171 - val_mean_absolute_error: 2.5597 Epoch 107/1000 7/7 [==============================] - 0s 18ms/step - loss: 3.9703 - mean_absolute_error: 1.5524 - val_loss: 12.3524 - val_mean_absolute_error: 2.6150 Epoch 108/1000 7/7 [==============================] - 0s 18ms/step - loss: 3.8778 - mean_absolute_error: 1.5254 - val_loss: 11.2249 - val_mean_absolute_error: 2.5648 Epoch 109/1000 7/7 [==============================] - 0s 18ms/step - loss: 4.0221 - mean_absolute_error: 1.5761 - val_loss: 12.2646 - val_mean_absolute_error: 2.6097 Epoch 110/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.0490 - mean_absolute_error: 1.5504 - val_loss: 11.5389 - val_mean_absolute_error: 2.6096 Epoch 111/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.8138 - mean_absolute_error: 1.5301 - val_loss: 12.1342 - val_mean_absolute_error: 2.5970 Epoch 112/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.1622 - mean_absolute_error: 1.5750 - val_loss: 13.2667 - val_mean_absolute_error: 2.7071 Epoch 113/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.0885 - mean_absolute_error: 1.5672 - val_loss: 11.6533 - val_mean_absolute_error: 2.5721 Epoch 114/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.7525 - mean_absolute_error: 1.5201 - val_loss: 12.5285 - val_mean_absolute_error: 2.7109 Epoch 115/1000 7/7 [==============================] - 0s 18ms/step - loss: 3.8603 - mean_absolute_error: 1.5266 - val_loss: 11.6242 - val_mean_absolute_error: 2.5741 Epoch 116/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.7306 - mean_absolute_error: 1.5031 - val_loss: 13.3945 - val_mean_absolute_error: 2.7457 Epoch 117/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.5558 - mean_absolute_error: 1.4591 - val_loss: 11.4685 - val_mean_absolute_error: 2.6445 Epoch 118/1000 7/7 [==============================] - 0s 20ms/step - loss: 3.3474 - mean_absolute_error: 1.4045 - val_loss: 13.1111 - val_mean_absolute_error: 2.7328 Epoch 119/1000 7/7 [==============================] - 0s 20ms/step - loss: 3.6341 - mean_absolute_error: 1.4573 - val_loss: 11.9714 - val_mean_absolute_error: 2.6572 Epoch 120/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.7191 - mean_absolute_error: 1.5119 - val_loss: 10.8905 - val_mean_absolute_error: 2.5671 Epoch 121/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.8713 - mean_absolute_error: 1.5122 - val_loss: 14.0087 - val_mean_absolute_error: 2.7923 Epoch 122/1000 7/7 [==============================] - 0s 18ms/step - loss: 3.4481 - mean_absolute_error: 1.4297 - val_loss: 12.9009 - val_mean_absolute_error: 2.7341 Epoch 123/1000 7/7 [==============================] - 0s 20ms/step - loss: 3.3946 - mean_absolute_error: 1.4364 - val_loss: 11.7029 - val_mean_absolute_error: 2.6106 Epoch 124/1000 7/7 [==============================] - 0s 20ms/step - loss: 3.4883 - mean_absolute_error: 1.4088 - val_loss: 12.5829 - val_mean_absolute_error: 2.7128 Epoch 125/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.4001 - mean_absolute_error: 1.3724 - val_loss: 12.1939 - val_mean_absolute_error: 2.7021 Epoch 126/1000 7/7 [==============================] - 0s 20ms/step - loss: 3.4151 - mean_absolute_error: 1.3940 - val_loss: 12.0603 - val_mean_absolute_error: 2.6669 Epoch 127/1000 7/7 [==============================] - 0s 18ms/step - loss: 3.1184 - mean_absolute_error: 1.3674 - val_loss: 11.7343 - val_mean_absolute_error: 2.6246 Epoch 128/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.3976 - mean_absolute_error: 1.3961 - val_loss: 12.6104 - val_mean_absolute_error: 2.7089 Epoch 129/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.5491 - mean_absolute_error: 1.3879 - val_loss: 11.7930 - val_mean_absolute_error: 2.6915 Epoch 130/1000 7/7 [==============================] - 0s 30ms/step - loss: 3.7652 - mean_absolute_error: 1.4397 - val_loss: 12.7435 - val_mean_absolute_error: 2.7260 Epoch 131/1000 7/7 [==============================] - 0s 18ms/step - loss: 3.1672 - mean_absolute_error: 1.3300 - val_loss: 12.3652 - val_mean_absolute_error: 2.6534 Epoch 132/1000 7/7 [==============================] - 0s 18ms/step - loss: 3.0748 - mean_absolute_error: 1.3445 - val_loss: 12.4414 - val_mean_absolute_error: 2.6567 Epoch 133/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.0797 - mean_absolute_error: 1.3099 - val_loss: 13.5403 - val_mean_absolute_error: 2.7910 Epoch 134/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.0331 - mean_absolute_error: 1.3318 - val_loss: 12.1799 - val_mean_absolute_error: 2.7048 Epoch 135/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.8836 - mean_absolute_error: 1.2769 - val_loss: 12.9637 - val_mean_absolute_error: 2.7898 Epoch 136/1000 7/7 [==============================] - 0s 18ms/step - loss: 2.8331 - mean_absolute_error: 1.2675 - val_loss: 12.7286 - val_mean_absolute_error: 2.7376 Epoch 137/1000 7/7 [==============================] - 0s 20ms/step - loss: 2.8881 - mean_absolute_error: 1.2839 - val_loss: 13.4722 - val_mean_absolute_error: 2.7826 Epoch 138/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.8206 - mean_absolute_error: 1.2756 - val_loss: 12.2125 - val_mean_absolute_error: 2.6902 Epoch 139/1000 7/7 [==============================] - 0s 21ms/step - loss: 2.9885 - mean_absolute_error: 1.2627 - val_loss: 12.3354 - val_mean_absolute_error: 2.7162 Epoch 140/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.8023 - mean_absolute_error: 1.2209 - val_loss: 12.9204 - val_mean_absolute_error: 2.7832 Epoch 141/1000 7/7 [==============================] - 0s 20ms/step - loss: 2.6619 - mean_absolute_error: 1.2111 - val_loss: 12.4418 - val_mean_absolute_error: 2.6655 Epoch 142/1000 7/7 [==============================] - 0s 20ms/step - loss: 2.8507 - mean_absolute_error: 1.2399 - val_loss: 14.4104 - val_mean_absolute_error: 2.8638 Epoch 143/1000 7/7 [==============================] - 0s 21ms/step - loss: 3.0199 - mean_absolute_error: 1.2940 - val_loss: 13.8784 - val_mean_absolute_error: 2.7768 Epoch 144/1000 7/7 [==============================] - 0s 18ms/step - loss: 3.2883 - mean_absolute_error: 1.3526 - val_loss: 11.9637 - val_mean_absolute_error: 2.5752 Epoch 145/1000 7/7 [==============================] - 0s 22ms/step - loss: 3.4011 - mean_absolute_error: 1.3674 - val_loss: 13.4032 - val_mean_absolute_error: 2.8923 Epoch 146/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.3570 - mean_absolute_error: 1.3161 - val_loss: 12.6101 - val_mean_absolute_error: 2.8191 Epoch 147/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.0590 - mean_absolute_error: 1.2918 - val_loss: 12.1756 - val_mean_absolute_error: 2.6564 Epoch 148/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.7857 - mean_absolute_error: 1.2836 - val_loss: 12.9471 - val_mean_absolute_error: 2.7466 Epoch 149/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.6422 - mean_absolute_error: 1.2116 - val_loss: 11.1142 - val_mean_absolute_error: 2.6027 Epoch 150/1000 7/7 [==============================] - 0s 30ms/step - loss: 2.6248 - mean_absolute_error: 1.2050 - val_loss: 11.9937 - val_mean_absolute_error: 2.7385 Epoch 151/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.5421 - mean_absolute_error: 1.1645 - val_loss: 12.4640 - val_mean_absolute_error: 2.7042 Epoch 152/1000 7/7 [==============================] - 0s 20ms/step - loss: 2.7422 - mean_absolute_error: 1.2409 - val_loss: 13.2452 - val_mean_absolute_error: 2.7613 Epoch 153/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.5027 - mean_absolute_error: 1.1782 - val_loss: 12.2873 - val_mean_absolute_error: 2.7067 Epoch 154/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.4517 - mean_absolute_error: 1.1412 - val_loss: 13.3298 - val_mean_absolute_error: 2.8262 Epoch 155/1000 7/7 [==============================] - 0s 20ms/step - loss: 2.3415 - mean_absolute_error: 1.1131 - val_loss: 13.5745 - val_mean_absolute_error: 2.8647 Epoch 156/1000 7/7 [==============================] - 0s 18ms/step - loss: 2.5749 - mean_absolute_error: 1.2094 - val_loss: 12.8705 - val_mean_absolute_error: 2.6524 Epoch 157/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.5554 - mean_absolute_error: 1.2186 - val_loss: 13.2883 - val_mean_absolute_error: 2.7331 Epoch 158/1000 7/7 [==============================] - 0s 20ms/step - loss: 2.3598 - mean_absolute_error: 1.1550 - val_loss: 12.3002 - val_mean_absolute_error: 2.7021 Epoch 159/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.4637 - mean_absolute_error: 1.1801 - val_loss: 12.6783 - val_mean_absolute_error: 2.7151 Epoch 160/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.5383 - mean_absolute_error: 1.1939 - val_loss: 13.9599 - val_mean_absolute_error: 2.7955 Epoch 161/1000 7/7 [==============================] - 0s 26ms/step - loss: 2.3373 - mean_absolute_error: 1.1548 - val_loss: 13.0394 - val_mean_absolute_error: 2.7147 Epoch 162/1000 7/7 [==============================] - 0s 26ms/step - loss: 2.2312 - mean_absolute_error: 1.1169 - val_loss: 13.0918 - val_mean_absolute_error: 2.7516 Epoch 163/1000 7/7 [==============================] - 0s 23ms/step - loss: 2.4024 - mean_absolute_error: 1.1518 - val_loss: 13.1048 - val_mean_absolute_error: 2.7361 Epoch 164/1000 7/7 [==============================] - 0s 22ms/step - loss: 2.2814 - mean_absolute_error: 1.1466 - val_loss: 12.8048 - val_mean_absolute_error: 2.7271 Epoch 165/1000 7/7 [==============================] - 0s 23ms/step - loss: 2.3748 - mean_absolute_error: 1.1398 - val_loss: 14.5632 - val_mean_absolute_error: 2.7724 Epoch 166/1000 7/7 [==============================] - 0s 21ms/step - loss: 2.4541 - mean_absolute_error: 1.1507 - val_loss: 13.0304 - val_mean_absolute_error: 2.6723 Epoch 167/1000 7/7 [==============================] - 0s 22ms/step - loss: 2.0612 - mean_absolute_error: 1.0723 - val_loss: 14.1075 - val_mean_absolute_error: 2.8420 Epoch 168/1000 7/7 [==============================] - 0s 23ms/step - loss: 2.4335 - mean_absolute_error: 1.1865 - val_loss: 12.8792 - val_mean_absolute_error: 2.6578 Epoch 169/1000 7/7 [==============================] - 0s 21ms/step - loss: 2.1815 - mean_absolute_error: 1.1049 - val_loss: 15.4137 - val_mean_absolute_error: 2.9736 Epoch 170/1000 7/7 [==============================] - 0s 23ms/step - loss: 2.0418 - mean_absolute_error: 1.0472 - val_loss: 13.8005 - val_mean_absolute_error: 2.8009 Epoch 171/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.9532 - mean_absolute_error: 0.9938 - val_loss: 14.0068 - val_mean_absolute_error: 2.8472 Epoch 172/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.9873 - mean_absolute_error: 1.0495 - val_loss: 12.3222 - val_mean_absolute_error: 2.6145 Epoch 173/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.9885 - mean_absolute_error: 1.0353 - val_loss: 13.7077 - val_mean_absolute_error: 2.7862 Epoch 174/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.8673 - mean_absolute_error: 0.9910 - val_loss: 13.6921 - val_mean_absolute_error: 2.7420 Epoch 175/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.1969 - mean_absolute_error: 1.0849 - val_loss: 13.3662 - val_mean_absolute_error: 2.7254 Epoch 176/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.8713 - mean_absolute_error: 1.0142 - val_loss: 15.2903 - val_mean_absolute_error: 2.9051 Epoch 177/1000 7/7 [==============================] - 0s 18ms/step - loss: 2.0599 - mean_absolute_error: 1.0423 - val_loss: 12.9603 - val_mean_absolute_error: 2.6559 Epoch 178/1000 7/7 [==============================] - 0s 20ms/step - loss: 2.1001 - mean_absolute_error: 1.0420 - val_loss: 13.5828 - val_mean_absolute_error: 2.8353 Epoch 179/1000 7/7 [==============================] - 0s 30ms/step - loss: 1.9582 - mean_absolute_error: 1.0317 - val_loss: 12.7807 - val_mean_absolute_error: 2.6364 Epoch 180/1000 7/7 [==============================] - 0s 20ms/step - loss: 2.0338 - mean_absolute_error: 1.0638 - val_loss: 14.0145 - val_mean_absolute_error: 2.8171 Epoch 181/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.8746 - mean_absolute_error: 0.9876 - val_loss: 13.6976 - val_mean_absolute_error: 2.8184 Epoch 182/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.7365 - mean_absolute_error: 0.9599 - val_loss: 14.4548 - val_mean_absolute_error: 2.9051 Epoch 183/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.7259 - mean_absolute_error: 0.9293 - val_loss: 14.2060 - val_mean_absolute_error: 2.8164 Epoch 184/1000 7/7 [==============================] - 0s 23ms/step - loss: 1.9007 - mean_absolute_error: 0.9507 - val_loss: 13.6237 - val_mean_absolute_error: 2.7271 Epoch 185/1000 7/7 [==============================] - 0s 21ms/step - loss: 1.9328 - mean_absolute_error: 0.9810 - val_loss: 15.0218 - val_mean_absolute_error: 2.9993 Epoch 186/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.2197 - mean_absolute_error: 1.0966 - val_loss: 13.3080 - val_mean_absolute_error: 2.6941 Epoch 187/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.1156 - mean_absolute_error: 1.0894 - val_loss: 13.9135 - val_mean_absolute_error: 2.7584 Epoch 188/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.2516 - mean_absolute_error: 1.0991 - val_loss: 13.2770 - val_mean_absolute_error: 2.7714 Epoch 189/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.8079 - mean_absolute_error: 0.9854 - val_loss: 15.2468 - val_mean_absolute_error: 2.9587 Epoch 190/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.8021 - mean_absolute_error: 0.9856 - val_loss: 13.5470 - val_mean_absolute_error: 2.7510 Epoch 191/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.6388 - mean_absolute_error: 0.9125 - val_loss: 13.7497 - val_mean_absolute_error: 2.8040 Epoch 192/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.5547 - mean_absolute_error: 0.9015 - val_loss: 14.2507 - val_mean_absolute_error: 2.8663 Epoch 193/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.6243 - mean_absolute_error: 0.9020 - val_loss: 14.2628 - val_mean_absolute_error: 2.8185 Epoch 194/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.5147 - mean_absolute_error: 0.8592 - val_loss: 14.2280 - val_mean_absolute_error: 2.8011 Epoch 195/1000 7/7 [==============================] - 0s 21ms/step - loss: 1.7417 - mean_absolute_error: 0.9261 - val_loss: 14.9307 - val_mean_absolute_error: 2.9519 Epoch 196/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.8947 - mean_absolute_error: 1.0164 - val_loss: 14.7871 - val_mean_absolute_error: 2.9151 Epoch 197/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.9367 - mean_absolute_error: 1.0255 - val_loss: 13.8581 - val_mean_absolute_error: 2.7242 Epoch 198/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.6423 - mean_absolute_error: 0.9306 - val_loss: 14.8666 - val_mean_absolute_error: 3.0509 Epoch 199/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.5193 - mean_absolute_error: 0.9141 - val_loss: 14.0924 - val_mean_absolute_error: 2.7342 Epoch 200/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.6421 - mean_absolute_error: 0.9297 - val_loss: 14.9141 - val_mean_absolute_error: 2.8639 Epoch 201/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.8521 - mean_absolute_error: 0.9863 - val_loss: 14.2642 - val_mean_absolute_error: 2.8467 Epoch 202/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.9017 - mean_absolute_error: 1.0165 - val_loss: 13.1774 - val_mean_absolute_error: 2.7270 Epoch 203/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.1549 - mean_absolute_error: 1.0425 - val_loss: 14.9773 - val_mean_absolute_error: 2.9437 Epoch 204/1000 7/7 [==============================] - 0s 21ms/step - loss: 1.5397 - mean_absolute_error: 0.9091 - val_loss: 13.9316 - val_mean_absolute_error: 2.8079 Epoch 205/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.6839 - mean_absolute_error: 0.9766 - val_loss: 14.4063 - val_mean_absolute_error: 2.9925 Epoch 206/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.6357 - mean_absolute_error: 0.9348 - val_loss: 14.0010 - val_mean_absolute_error: 2.8300 Epoch 207/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.5466 - mean_absolute_error: 0.8789 - val_loss: 14.2251 - val_mean_absolute_error: 2.7963 Epoch 208/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.6866 - mean_absolute_error: 0.9034 - val_loss: 13.8348 - val_mean_absolute_error: 2.8010 Epoch 209/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.4978 - mean_absolute_error: 0.8927 - val_loss: 14.7813 - val_mean_absolute_error: 2.9321 Epoch 210/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.5405 - mean_absolute_error: 0.9127 - val_loss: 14.3245 - val_mean_absolute_error: 2.7944 Epoch 211/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.5005 - mean_absolute_error: 0.8884 - val_loss: 14.6576 - val_mean_absolute_error: 2.9000 Epoch 212/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.2763 - mean_absolute_error: 0.8172 - val_loss: 13.7831 - val_mean_absolute_error: 2.7710 Epoch 213/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.3272 - mean_absolute_error: 0.8402 - val_loss: 15.5701 - val_mean_absolute_error: 3.0109 Epoch 214/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.3822 - mean_absolute_error: 0.8589 - val_loss: 14.6657 - val_mean_absolute_error: 2.8942 Epoch 215/1000 7/7 [==============================] - 0s 22ms/step - loss: 1.7019 - mean_absolute_error: 0.9370 - val_loss: 14.3298 - val_mean_absolute_error: 2.8113 Epoch 216/1000 7/7 [==============================] - 0s 20ms/step - loss: 2.1503 - mean_absolute_error: 0.9328 - val_loss: 16.3933 - val_mean_absolute_error: 3.0976 Epoch 217/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.6521 - mean_absolute_error: 0.9415 - val_loss: 14.2364 - val_mean_absolute_error: 2.7802 Epoch 218/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.4702 - mean_absolute_error: 0.9206 - val_loss: 15.7740 - val_mean_absolute_error: 3.0242 Epoch 219/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.3940 - mean_absolute_error: 0.8803 - val_loss: 15.0103 - val_mean_absolute_error: 2.8447 Epoch 220/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.2756 - mean_absolute_error: 0.8146 - val_loss: 15.4107 - val_mean_absolute_error: 3.0069 Epoch 221/1000 7/7 [==============================] - 0s 25ms/step - loss: 1.3417 - mean_absolute_error: 0.8307 - val_loss: 14.2270 - val_mean_absolute_error: 2.8350 Epoch 222/1000 7/7 [==============================] - 0s 22ms/step - loss: 1.1758 - mean_absolute_error: 0.7610 - val_loss: 15.7742 - val_mean_absolute_error: 2.9761 Epoch 223/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.2007 - mean_absolute_error: 0.7830 - val_loss: 15.2184 - val_mean_absolute_error: 2.9057 Epoch 224/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.2224 - mean_absolute_error: 0.7676 - val_loss: 14.4776 - val_mean_absolute_error: 2.8340 Epoch 225/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.4082 - mean_absolute_error: 0.8458 - val_loss: 14.4805 - val_mean_absolute_error: 2.8767 Epoch 226/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.2980 - mean_absolute_error: 0.8263 - val_loss: 15.7655 - val_mean_absolute_error: 3.0428 Epoch 227/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.6383 - mean_absolute_error: 0.9247 - val_loss: 14.4220 - val_mean_absolute_error: 2.7741 Epoch 228/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.5811 - mean_absolute_error: 0.9161 - val_loss: 15.5395 - val_mean_absolute_error: 3.0118 Epoch 229/1000 7/7 [==============================] - 0s 30ms/step - loss: 1.4880 - mean_absolute_error: 0.8699 - val_loss: 14.9694 - val_mean_absolute_error: 2.9395 Epoch 230/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.5611 - mean_absolute_error: 0.9404 - val_loss: 14.1237 - val_mean_absolute_error: 2.7715 Epoch 231/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.2579 - mean_absolute_error: 1.1156 - val_loss: 16.7146 - val_mean_absolute_error: 3.3250 Epoch 232/1000 7/7 [==============================] - 0s 20ms/step - loss: 2.5740 - mean_absolute_error: 1.2576 - val_loss: 14.4687 - val_mean_absolute_error: 2.7551 Epoch 233/1000 7/7 [==============================] - 0s 25ms/step - loss: 3.8510 - mean_absolute_error: 1.3761 - val_loss: 15.4115 - val_mean_absolute_error: 2.9939 Epoch 234/1000 7/7 [==============================] - 0s 29ms/step - loss: 2.2850 - mean_absolute_error: 1.1216 - val_loss: 14.7552 - val_mean_absolute_error: 2.8406 Epoch 235/1000 7/7 [==============================] - 0s 23ms/step - loss: 1.7677 - mean_absolute_error: 1.0060 - val_loss: 15.8288 - val_mean_absolute_error: 3.0458 Epoch 236/1000 7/7 [==============================] - 0s 24ms/step - loss: 1.5349 - mean_absolute_error: 0.9227 - val_loss: 13.6365 - val_mean_absolute_error: 2.7268 Epoch 237/1000 7/7 [==============================] - 0s 21ms/step - loss: 1.3306 - mean_absolute_error: 0.8358 - val_loss: 15.3777 - val_mean_absolute_error: 2.9796 Epoch 238/1000 7/7 [==============================] - 0s 21ms/step - loss: 1.3564 - mean_absolute_error: 0.8291 - val_loss: 14.0862 - val_mean_absolute_error: 2.8010 Epoch 239/1000 7/7 [==============================] - 0s 21ms/step - loss: 1.1859 - mean_absolute_error: 0.7785 - val_loss: 13.9662 - val_mean_absolute_error: 2.8136 Epoch 240/1000 7/7 [==============================] - 0s 23ms/step - loss: 1.1614 - mean_absolute_error: 0.7607 - val_loss: 14.0564 - val_mean_absolute_error: 2.7858 Epoch 241/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.1536 - mean_absolute_error: 0.7767 - val_loss: 14.7293 - val_mean_absolute_error: 2.8781 Epoch 242/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.1018 - mean_absolute_error: 0.7357 - val_loss: 14.9157 - val_mean_absolute_error: 2.9550 Epoch 243/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.9745 - mean_absolute_error: 0.6784 - val_loss: 15.5461 - val_mean_absolute_error: 2.8745 Epoch 244/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.9645 - mean_absolute_error: 0.6693 - val_loss: 15.0168 - val_mean_absolute_error: 2.9746 Epoch 245/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.0143 - mean_absolute_error: 0.6795 - val_loss: 14.8649 - val_mean_absolute_error: 2.8693 Epoch 246/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.0080 - mean_absolute_error: 0.6942 - val_loss: 14.6346 - val_mean_absolute_error: 2.7982 Epoch 247/1000 7/7 [==============================] - 0s 21ms/step - loss: 1.0619 - mean_absolute_error: 0.7199 - val_loss: 15.1219 - val_mean_absolute_error: 2.9749 Epoch 248/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.2095 - mean_absolute_error: 0.7731 - val_loss: 14.1458 - val_mean_absolute_error: 2.8126 Epoch 249/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.1629 - mean_absolute_error: 0.7667 - val_loss: 15.4341 - val_mean_absolute_error: 2.9715 Epoch 250/1000 7/7 [==============================] - 0s 23ms/step - loss: 1.2383 - mean_absolute_error: 0.8085 - val_loss: 14.2865 - val_mean_absolute_error: 2.8919 Epoch 251/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.2421 - mean_absolute_error: 0.8277 - val_loss: 15.3616 - val_mean_absolute_error: 2.9923 Epoch 252/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.1956 - mean_absolute_error: 0.7607 - val_loss: 14.7350 - val_mean_absolute_error: 2.8229 Epoch 253/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.1231 - mean_absolute_error: 0.7367 - val_loss: 14.1532 - val_mean_absolute_error: 2.8366 Epoch 254/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.9324 - mean_absolute_error: 0.6504 - val_loss: 14.6484 - val_mean_absolute_error: 2.8747 Epoch 255/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.0646 - mean_absolute_error: 0.7093 - val_loss: 14.9079 - val_mean_absolute_error: 2.8950 Epoch 256/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.8943 - mean_absolute_error: 0.6425 - val_loss: 14.6101 - val_mean_absolute_error: 2.8533 Epoch 257/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.9035 - mean_absolute_error: 0.6422 - val_loss: 14.8931 - val_mean_absolute_error: 2.8588 Epoch 258/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.9626 - mean_absolute_error: 0.6650 - val_loss: 14.6791 - val_mean_absolute_error: 2.8662 Epoch 259/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.0232 - mean_absolute_error: 0.7010 - val_loss: 15.0942 - val_mean_absolute_error: 2.8759 Epoch 260/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.0783 - mean_absolute_error: 0.7030 - val_loss: 14.0869 - val_mean_absolute_error: 2.8905 Epoch 261/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.9616 - mean_absolute_error: 0.6819 - val_loss: 14.8983 - val_mean_absolute_error: 2.9047 Epoch 262/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.9837 - mean_absolute_error: 0.6881 - val_loss: 15.5244 - val_mean_absolute_error: 3.0657 Epoch 263/1000 7/7 [==============================] - 0s 26ms/step - loss: 1.2888 - mean_absolute_error: 0.8004 - val_loss: 13.7884 - val_mean_absolute_error: 2.8221 Epoch 264/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.2546 - mean_absolute_error: 0.7493 - val_loss: 14.6765 - val_mean_absolute_error: 3.0004 Epoch 265/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.0026 - mean_absolute_error: 0.7151 - val_loss: 14.3838 - val_mean_absolute_error: 2.8242 Epoch 266/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.9443 - mean_absolute_error: 0.6796 - val_loss: 15.6352 - val_mean_absolute_error: 3.1169 Epoch 267/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.9576 - mean_absolute_error: 0.7224 - val_loss: 13.5848 - val_mean_absolute_error: 2.7225 Epoch 268/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.9882 - mean_absolute_error: 0.6677 - val_loss: 14.7937 - val_mean_absolute_error: 2.9037 Epoch 269/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.9179 - mean_absolute_error: 0.6338 - val_loss: 14.8672 - val_mean_absolute_error: 2.9118 Epoch 270/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.8260 - mean_absolute_error: 0.6211 - val_loss: 13.7695 - val_mean_absolute_error: 2.7538 Epoch 271/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.1958 - mean_absolute_error: 0.6820 - val_loss: 15.4077 - val_mean_absolute_error: 2.9772 Epoch 272/1000 7/7 [==============================] - 0s 21ms/step - loss: 1.6269 - mean_absolute_error: 0.7616 - val_loss: 14.9109 - val_mean_absolute_error: 2.8642 Epoch 273/1000 7/7 [==============================] - 0s 21ms/step - loss: 1.0528 - mean_absolute_error: 0.7576 - val_loss: 15.1503 - val_mean_absolute_error: 2.9716 Epoch 274/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.1252 - mean_absolute_error: 0.7180 - val_loss: 14.3404 - val_mean_absolute_error: 2.8342 Epoch 275/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.9894 - mean_absolute_error: 0.6830 - val_loss: 15.5529 - val_mean_absolute_error: 3.0350 Epoch 276/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.9125 - mean_absolute_error: 0.6426 - val_loss: 14.8019 - val_mean_absolute_error: 2.8413 Epoch 277/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.8621 - mean_absolute_error: 0.6476 - val_loss: 14.4121 - val_mean_absolute_error: 2.8434 Epoch 278/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.8364 - mean_absolute_error: 0.6459 - val_loss: 14.7961 - val_mean_absolute_error: 2.9094 Epoch 279/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.8193 - mean_absolute_error: 0.6404 - val_loss: 14.4032 - val_mean_absolute_error: 2.8262 Epoch 280/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.0442 - mean_absolute_error: 0.7197 - val_loss: 14.6968 - val_mean_absolute_error: 2.7982 Epoch 281/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.9785 - mean_absolute_error: 0.6976 - val_loss: 15.1016 - val_mean_absolute_error: 3.0245 Epoch 282/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.0210 - mean_absolute_error: 0.7094 - val_loss: 14.8584 - val_mean_absolute_error: 2.9468 Epoch 283/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.8566 - mean_absolute_error: 0.6541 - val_loss: 14.0730 - val_mean_absolute_error: 2.7784 Epoch 284/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.7958 - mean_absolute_error: 0.6235 - val_loss: 14.8787 - val_mean_absolute_error: 2.9511 Epoch 285/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.0381 - mean_absolute_error: 0.6915 - val_loss: 14.5259 - val_mean_absolute_error: 2.8605 Epoch 286/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.3503 - mean_absolute_error: 0.7670 - val_loss: 15.2049 - val_mean_absolute_error: 2.9682 Epoch 287/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.8944 - mean_absolute_error: 0.6985 - val_loss: 13.6927 - val_mean_absolute_error: 2.8336 Epoch 288/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.8793 - mean_absolute_error: 0.6504 - val_loss: 14.9116 - val_mean_absolute_error: 3.0028 Epoch 289/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.0046 - mean_absolute_error: 0.6926 - val_loss: 15.3686 - val_mean_absolute_error: 2.9122 Epoch 290/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.8954 - mean_absolute_error: 0.6464 - val_loss: 14.0229 - val_mean_absolute_error: 2.8282 Epoch 291/1000 7/7 [==============================] - 0s 35ms/step - loss: 1.0916 - mean_absolute_error: 0.6758 - val_loss: 14.8383 - val_mean_absolute_error: 2.9217 Epoch 292/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.9535 - mean_absolute_error: 0.6681 - val_loss: 13.8383 - val_mean_absolute_error: 2.8656 Epoch 293/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.8694 - mean_absolute_error: 0.6223 - val_loss: 13.9622 - val_mean_absolute_error: 2.8714 Epoch 294/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.7727 - mean_absolute_error: 0.5858 - val_loss: 15.1241 - val_mean_absolute_error: 2.9218 Epoch 295/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.7843 - mean_absolute_error: 0.5716 - val_loss: 13.9671 - val_mean_absolute_error: 2.8031 Epoch 296/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.7250 - mean_absolute_error: 0.5623 - val_loss: 14.3183 - val_mean_absolute_error: 2.9810 Epoch 297/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.6994 - mean_absolute_error: 0.5580 - val_loss: 14.2512 - val_mean_absolute_error: 2.7489 Epoch 298/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.7791 - mean_absolute_error: 0.5980 - val_loss: 14.4619 - val_mean_absolute_error: 3.0089 Epoch 299/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.7861 - mean_absolute_error: 0.6093 - val_loss: 14.2137 - val_mean_absolute_error: 2.8643 Epoch 300/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.6893 - mean_absolute_error: 0.5760 - val_loss: 14.4391 - val_mean_absolute_error: 2.8727 Epoch 301/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.6561 - mean_absolute_error: 0.5337 - val_loss: 14.5410 - val_mean_absolute_error: 2.9610 Epoch 302/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.6926 - mean_absolute_error: 0.5603 - val_loss: 13.9194 - val_mean_absolute_error: 2.8174 Epoch 303/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.6357 - mean_absolute_error: 0.5364 - val_loss: 14.4482 - val_mean_absolute_error: 2.9114 Epoch 304/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.6600 - mean_absolute_error: 0.5458 - val_loss: 14.3765 - val_mean_absolute_error: 2.8428 Epoch 305/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.7172 - mean_absolute_error: 0.5743 - val_loss: 15.3226 - val_mean_absolute_error: 2.9731 Epoch 306/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.7737 - mean_absolute_error: 0.6136 - val_loss: 14.2748 - val_mean_absolute_error: 2.8491 Epoch 307/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.8235 - mean_absolute_error: 0.6262 - val_loss: 14.1584 - val_mean_absolute_error: 2.7963 Epoch 308/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.8300 - mean_absolute_error: 0.6271 - val_loss: 15.0316 - val_mean_absolute_error: 3.0267 Epoch 309/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.6591 - mean_absolute_error: 0.5414 - val_loss: 14.2179 - val_mean_absolute_error: 2.8809 Epoch 310/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.7143 - mean_absolute_error: 0.5680 - val_loss: 13.4670 - val_mean_absolute_error: 2.7884 Epoch 311/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.6494 - mean_absolute_error: 0.5607 - val_loss: 15.2140 - val_mean_absolute_error: 2.9945 Epoch 312/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.0001 - mean_absolute_error: 0.6376 - val_loss: 14.2290 - val_mean_absolute_error: 2.7742 Epoch 313/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.0495 - mean_absolute_error: 0.6808 - val_loss: 13.2752 - val_mean_absolute_error: 2.9306 Epoch 314/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.8175 - mean_absolute_error: 0.6267 - val_loss: 14.4751 - val_mean_absolute_error: 2.8879 Epoch 315/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.3225 - mean_absolute_error: 0.7680 - val_loss: 14.7107 - val_mean_absolute_error: 2.8287 Epoch 316/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.8211 - mean_absolute_error: 0.6542 - val_loss: 13.6500 - val_mean_absolute_error: 2.8660 Epoch 317/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.8346 - mean_absolute_error: 0.6348 - val_loss: 13.8961 - val_mean_absolute_error: 2.8832 Epoch 318/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.8098 - mean_absolute_error: 0.6288 - val_loss: 15.0272 - val_mean_absolute_error: 2.8902 Epoch 319/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.7009 - mean_absolute_error: 0.5890 - val_loss: 13.8675 - val_mean_absolute_error: 2.8434 Epoch 320/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.6661 - mean_absolute_error: 0.5453 - val_loss: 14.2193 - val_mean_absolute_error: 2.8618 Epoch 321/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.6166 - mean_absolute_error: 0.5355 - val_loss: 13.5175 - val_mean_absolute_error: 2.7696 Epoch 322/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.7430 - mean_absolute_error: 0.5960 - val_loss: 14.4607 - val_mean_absolute_error: 2.9581 Epoch 323/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.7392 - mean_absolute_error: 0.6133 - val_loss: 13.7395 - val_mean_absolute_error: 2.7658 Epoch 324/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.7276 - mean_absolute_error: 0.5715 - val_loss: 14.9507 - val_mean_absolute_error: 3.0139 Epoch 325/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.1875 - mean_absolute_error: 0.7549 - val_loss: 13.9561 - val_mean_absolute_error: 2.7930 Epoch 326/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.2626 - mean_absolute_error: 0.8518 - val_loss: 15.1441 - val_mean_absolute_error: 3.0035 Epoch 327/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.4699 - mean_absolute_error: 0.8510 - val_loss: 13.8350 - val_mean_absolute_error: 2.8456 Epoch 328/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.0256 - mean_absolute_error: 0.8333 - val_loss: 12.7075 - val_mean_absolute_error: 2.6183 Epoch 329/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.3759 - mean_absolute_error: 0.7640 - val_loss: 13.7991 - val_mean_absolute_error: 2.8305 Epoch 330/1000 7/7 [==============================] - 0s 17ms/step - loss: 1.0591 - mean_absolute_error: 0.7230 - val_loss: 13.9768 - val_mean_absolute_error: 2.8674 Epoch 331/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.9191 - mean_absolute_error: 0.6716 - val_loss: 13.4606 - val_mean_absolute_error: 2.7244 Epoch 332/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.0174 - mean_absolute_error: 0.6963 - val_loss: 15.4180 - val_mean_absolute_error: 3.0912 Epoch 333/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.8865 - mean_absolute_error: 0.6713 - val_loss: 13.9525 - val_mean_absolute_error: 2.8219 Epoch 334/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.8465 - mean_absolute_error: 0.6283 - val_loss: 13.8559 - val_mean_absolute_error: 2.9138 Epoch 335/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.6963 - mean_absolute_error: 0.5739 - val_loss: 14.5172 - val_mean_absolute_error: 2.9263 Epoch 336/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.6934 - mean_absolute_error: 0.5434 - val_loss: 13.8758 - val_mean_absolute_error: 2.8171 Epoch 337/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.8056 - mean_absolute_error: 0.5816 - val_loss: 14.8534 - val_mean_absolute_error: 2.9148 Epoch 338/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.9246 - mean_absolute_error: 0.6810 - val_loss: 13.8262 - val_mean_absolute_error: 2.8329 Epoch 339/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.1540 - mean_absolute_error: 0.6623 - val_loss: 14.1131 - val_mean_absolute_error: 2.8892 Epoch 340/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.8899 - mean_absolute_error: 0.6227 - val_loss: 14.3756 - val_mean_absolute_error: 2.8925 Epoch 341/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.7530 - mean_absolute_error: 0.5742 - val_loss: 14.3370 - val_mean_absolute_error: 2.8980 Epoch 342/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5845 - mean_absolute_error: 0.5024 - val_loss: 14.0896 - val_mean_absolute_error: 2.9056 Epoch 343/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4595 - mean_absolute_error: 0.4649 - val_loss: 13.6928 - val_mean_absolute_error: 2.7660 Epoch 344/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5516 - mean_absolute_error: 0.4920 - val_loss: 14.5258 - val_mean_absolute_error: 2.8632 Epoch 345/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.5675 - mean_absolute_error: 0.5298 - val_loss: 15.3427 - val_mean_absolute_error: 3.0418 Epoch 346/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.6827 - mean_absolute_error: 0.6121 - val_loss: 13.3984 - val_mean_absolute_error: 2.6906 Epoch 347/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.6696 - mean_absolute_error: 0.5754 - val_loss: 15.4782 - val_mean_absolute_error: 3.0774 Epoch 348/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5841 - mean_absolute_error: 0.5148 - val_loss: 14.4647 - val_mean_absolute_error: 2.8107 Epoch 349/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.5947 - mean_absolute_error: 0.5183 - val_loss: 13.9929 - val_mean_absolute_error: 2.8311 Epoch 350/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5067 - mean_absolute_error: 0.4745 - val_loss: 14.2053 - val_mean_absolute_error: 2.9430 Epoch 351/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.5622 - mean_absolute_error: 0.4838 - val_loss: 14.7118 - val_mean_absolute_error: 2.8573 Epoch 352/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.5515 - mean_absolute_error: 0.5255 - val_loss: 14.1842 - val_mean_absolute_error: 2.9134 Epoch 353/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5223 - mean_absolute_error: 0.5017 - val_loss: 14.7925 - val_mean_absolute_error: 2.9353 Epoch 354/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.5779 - mean_absolute_error: 0.5425 - val_loss: 13.9457 - val_mean_absolute_error: 2.8297 Epoch 355/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.5620 - mean_absolute_error: 0.5253 - val_loss: 14.2021 - val_mean_absolute_error: 2.8060 Epoch 356/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.5203 - mean_absolute_error: 0.4996 - val_loss: 14.2668 - val_mean_absolute_error: 2.9188 Epoch 357/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.4797 - mean_absolute_error: 0.4591 - val_loss: 13.8774 - val_mean_absolute_error: 2.8155 Epoch 358/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4502 - mean_absolute_error: 0.4704 - val_loss: 13.9423 - val_mean_absolute_error: 2.7980 Epoch 359/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.5024 - mean_absolute_error: 0.4525 - val_loss: 13.8372 - val_mean_absolute_error: 2.8386 Epoch 360/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.4193 - mean_absolute_error: 0.4160 - val_loss: 14.8512 - val_mean_absolute_error: 2.9842 Epoch 361/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4879 - mean_absolute_error: 0.4726 - val_loss: 13.5405 - val_mean_absolute_error: 2.8023 Epoch 362/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.4405 - mean_absolute_error: 0.4496 - val_loss: 13.1217 - val_mean_absolute_error: 2.6877 Epoch 363/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4909 - mean_absolute_error: 0.5243 - val_loss: 15.4354 - val_mean_absolute_error: 3.1275 Epoch 364/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.7021 - mean_absolute_error: 0.6102 - val_loss: 14.2164 - val_mean_absolute_error: 2.8535 Epoch 365/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.4336 - mean_absolute_error: 0.4718 - val_loss: 13.9973 - val_mean_absolute_error: 2.8131 Epoch 366/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.3985 - mean_absolute_error: 0.4484 - val_loss: 14.9832 - val_mean_absolute_error: 3.0101 Epoch 367/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4042 - mean_absolute_error: 0.4362 - val_loss: 13.5860 - val_mean_absolute_error: 2.7392 Epoch 368/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.6578 - mean_absolute_error: 0.5044 - val_loss: 14.4004 - val_mean_absolute_error: 2.9444 Epoch 369/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5473 - mean_absolute_error: 0.4811 - val_loss: 15.0652 - val_mean_absolute_error: 2.9398 Epoch 370/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.6271 - mean_absolute_error: 0.5090 - val_loss: 12.9193 - val_mean_absolute_error: 2.7545 Epoch 371/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.6057 - mean_absolute_error: 0.5713 - val_loss: 14.8020 - val_mean_absolute_error: 2.9458 Epoch 372/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5893 - mean_absolute_error: 0.5567 - val_loss: 14.1247 - val_mean_absolute_error: 2.8082 Epoch 373/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.4541 - mean_absolute_error: 0.4768 - val_loss: 13.8466 - val_mean_absolute_error: 2.8314 Epoch 374/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4464 - mean_absolute_error: 0.4487 - val_loss: 13.4042 - val_mean_absolute_error: 2.8019 Epoch 375/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.4280 - mean_absolute_error: 0.4422 - val_loss: 13.9582 - val_mean_absolute_error: 2.8649 Epoch 376/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.3902 - mean_absolute_error: 0.4365 - val_loss: 13.9530 - val_mean_absolute_error: 2.8392 Epoch 377/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.4562 - mean_absolute_error: 0.4306 - val_loss: 13.6028 - val_mean_absolute_error: 2.8242 Epoch 378/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.4664 - mean_absolute_error: 0.4565 - val_loss: 14.9062 - val_mean_absolute_error: 2.9799 Epoch 379/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.4533 - mean_absolute_error: 0.4719 - val_loss: 13.8565 - val_mean_absolute_error: 2.8127 Epoch 380/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.4554 - mean_absolute_error: 0.4641 - val_loss: 13.9057 - val_mean_absolute_error: 2.8217 Epoch 381/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.4783 - mean_absolute_error: 0.4774 - val_loss: 14.4116 - val_mean_absolute_error: 2.9013 Epoch 382/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.5298 - mean_absolute_error: 0.4625 - val_loss: 13.8276 - val_mean_absolute_error: 2.7782 Epoch 383/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.5947 - mean_absolute_error: 0.4633 - val_loss: 15.0547 - val_mean_absolute_error: 3.1129 Epoch 384/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.5894 - mean_absolute_error: 0.5010 - val_loss: 14.1438 - val_mean_absolute_error: 2.7943 Epoch 385/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.6262 - mean_absolute_error: 0.5553 - val_loss: 13.9962 - val_mean_absolute_error: 2.7794 Epoch 386/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.6834 - mean_absolute_error: 0.5769 - val_loss: 14.8004 - val_mean_absolute_error: 2.9307 Epoch 387/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.7117 - mean_absolute_error: 0.5997 - val_loss: 15.2666 - val_mean_absolute_error: 2.9156 Epoch 388/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.6189 - mean_absolute_error: 0.5978 - val_loss: 15.0259 - val_mean_absolute_error: 2.9536 Epoch 389/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.7670 - mean_absolute_error: 0.6159 - val_loss: 13.5652 - val_mean_absolute_error: 2.7910 Epoch 390/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.6400 - mean_absolute_error: 0.6199 - val_loss: 15.2481 - val_mean_absolute_error: 3.0441 Epoch 391/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.5914 - mean_absolute_error: 0.6066 - val_loss: 14.1801 - val_mean_absolute_error: 3.0144 Epoch 392/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.6175 - mean_absolute_error: 0.5293 - val_loss: 14.2531 - val_mean_absolute_error: 2.9052 Epoch 393/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4620 - mean_absolute_error: 0.4856 - val_loss: 14.7249 - val_mean_absolute_error: 2.9266 Epoch 394/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5434 - mean_absolute_error: 0.4659 - val_loss: 13.6665 - val_mean_absolute_error: 2.9400 Epoch 395/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.0678 - mean_absolute_error: 0.5315 - val_loss: 13.1837 - val_mean_absolute_error: 2.7414 Epoch 396/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5992 - mean_absolute_error: 0.5039 - val_loss: 14.4124 - val_mean_absolute_error: 2.8134 Epoch 397/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5837 - mean_absolute_error: 0.4720 - val_loss: 14.4903 - val_mean_absolute_error: 2.9060 Epoch 398/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.5538 - mean_absolute_error: 0.4912 - val_loss: 14.1971 - val_mean_absolute_error: 2.8836 Epoch 399/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.7206 - mean_absolute_error: 0.5969 - val_loss: 14.5632 - val_mean_absolute_error: 2.9084 Epoch 400/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.7452 - mean_absolute_error: 0.6178 - val_loss: 13.0755 - val_mean_absolute_error: 2.8222 Epoch 401/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.8869 - mean_absolute_error: 0.6185 - val_loss: 14.0143 - val_mean_absolute_error: 2.8313 Epoch 402/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.5624 - mean_absolute_error: 0.5249 - val_loss: 14.2398 - val_mean_absolute_error: 2.9876 Epoch 403/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.6037 - mean_absolute_error: 0.5750 - val_loss: 13.9209 - val_mean_absolute_error: 2.8381 Epoch 404/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.5616 - mean_absolute_error: 0.5415 - val_loss: 13.8407 - val_mean_absolute_error: 2.9255 Epoch 405/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.5451 - mean_absolute_error: 0.5556 - val_loss: 14.2401 - val_mean_absolute_error: 2.8193 Epoch 406/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4820 - mean_absolute_error: 0.4960 - val_loss: 13.7601 - val_mean_absolute_error: 2.9317 Epoch 407/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.5015 - mean_absolute_error: 0.4420 - val_loss: 14.5032 - val_mean_absolute_error: 3.0502 Epoch 408/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.4607 - mean_absolute_error: 0.4567 - val_loss: 13.6502 - val_mean_absolute_error: 2.7737 Epoch 409/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.3785 - mean_absolute_error: 0.4185 - val_loss: 13.9544 - val_mean_absolute_error: 2.8786 Epoch 410/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3941 - mean_absolute_error: 0.4190 - val_loss: 14.1925 - val_mean_absolute_error: 2.9114 Epoch 411/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3670 - mean_absolute_error: 0.4035 - val_loss: 13.5518 - val_mean_absolute_error: 2.7303 Epoch 412/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3319 - mean_absolute_error: 0.3618 - val_loss: 13.7443 - val_mean_absolute_error: 2.8821 Epoch 413/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.3272 - mean_absolute_error: 0.3910 - val_loss: 13.6265 - val_mean_absolute_error: 2.7471 Epoch 414/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2661 - mean_absolute_error: 0.3425 - val_loss: 12.9470 - val_mean_absolute_error: 2.7007 Epoch 415/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2878 - mean_absolute_error: 0.3556 - val_loss: 14.6028 - val_mean_absolute_error: 2.9881 Epoch 416/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3063 - mean_absolute_error: 0.3658 - val_loss: 13.5431 - val_mean_absolute_error: 2.8467 Epoch 417/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2911 - mean_absolute_error: 0.3452 - val_loss: 14.4358 - val_mean_absolute_error: 2.9185 Epoch 418/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3333 - mean_absolute_error: 0.3641 - val_loss: 13.2179 - val_mean_absolute_error: 2.7792 Epoch 419/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2790 - mean_absolute_error: 0.3677 - val_loss: 13.3728 - val_mean_absolute_error: 2.7860 Epoch 420/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2430 - mean_absolute_error: 0.3373 - val_loss: 14.3271 - val_mean_absolute_error: 2.9191 Epoch 421/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2646 - mean_absolute_error: 0.3459 - val_loss: 13.1163 - val_mean_absolute_error: 2.7634 Epoch 422/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3635 - mean_absolute_error: 0.3740 - val_loss: 13.5994 - val_mean_absolute_error: 2.8216 Epoch 423/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.1488 - mean_absolute_error: 0.5540 - val_loss: 14.0456 - val_mean_absolute_error: 2.8183 Epoch 424/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.7083 - mean_absolute_error: 0.5489 - val_loss: 12.3500 - val_mean_absolute_error: 2.7339 Epoch 425/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.7859 - mean_absolute_error: 0.5303 - val_loss: 14.0167 - val_mean_absolute_error: 2.8275 Epoch 426/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.7110 - mean_absolute_error: 0.5652 - val_loss: 13.7333 - val_mean_absolute_error: 2.8561 Epoch 427/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.4642 - mean_absolute_error: 0.5051 - val_loss: 13.2311 - val_mean_absolute_error: 2.7714 Epoch 428/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.4186 - mean_absolute_error: 0.4744 - val_loss: 12.6010 - val_mean_absolute_error: 2.6881 Epoch 429/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4804 - mean_absolute_error: 0.4789 - val_loss: 13.9591 - val_mean_absolute_error: 2.8846 Epoch 430/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3727 - mean_absolute_error: 0.4315 - val_loss: 13.0936 - val_mean_absolute_error: 2.7942 Epoch 431/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.3012 - mean_absolute_error: 0.3906 - val_loss: 13.1466 - val_mean_absolute_error: 2.7782 Epoch 432/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3104 - mean_absolute_error: 0.3616 - val_loss: 13.0090 - val_mean_absolute_error: 2.7647 Epoch 433/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3312 - mean_absolute_error: 0.3762 - val_loss: 13.4493 - val_mean_absolute_error: 2.8733 Epoch 434/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.5373 - mean_absolute_error: 0.4118 - val_loss: 13.4472 - val_mean_absolute_error: 2.8477 Epoch 435/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4227 - mean_absolute_error: 0.3848 - val_loss: 14.4874 - val_mean_absolute_error: 2.9819 Epoch 436/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4148 - mean_absolute_error: 0.4069 - val_loss: 14.7439 - val_mean_absolute_error: 3.0063 Epoch 437/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5458 - mean_absolute_error: 0.4976 - val_loss: 13.8047 - val_mean_absolute_error: 2.8910 Epoch 438/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.4014 - mean_absolute_error: 0.4233 - val_loss: 13.5312 - val_mean_absolute_error: 2.8067 Epoch 439/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.5031 - mean_absolute_error: 0.4869 - val_loss: 14.1119 - val_mean_absolute_error: 2.9569 Epoch 440/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.4046 - mean_absolute_error: 0.4101 - val_loss: 14.2625 - val_mean_absolute_error: 2.8925 Epoch 441/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.4145 - mean_absolute_error: 0.4263 - val_loss: 13.5904 - val_mean_absolute_error: 2.9163 Epoch 442/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4678 - mean_absolute_error: 0.4958 - val_loss: 14.7541 - val_mean_absolute_error: 3.0457 Epoch 443/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5844 - mean_absolute_error: 0.4777 - val_loss: 13.1424 - val_mean_absolute_error: 2.8373 Epoch 444/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4749 - mean_absolute_error: 0.4533 - val_loss: 13.8761 - val_mean_absolute_error: 2.9936 Epoch 445/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3960 - mean_absolute_error: 0.3684 - val_loss: 13.8070 - val_mean_absolute_error: 2.9845 Epoch 446/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.4010 - mean_absolute_error: 0.3959 - val_loss: 14.1135 - val_mean_absolute_error: 2.9467 Epoch 447/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.3323 - mean_absolute_error: 0.3387 - val_loss: 13.7232 - val_mean_absolute_error: 2.9043 Epoch 448/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.3072 - mean_absolute_error: 0.3207 - val_loss: 13.8845 - val_mean_absolute_error: 2.9155 Epoch 449/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.2985 - mean_absolute_error: 0.2973 - val_loss: 14.0103 - val_mean_absolute_error: 2.9276 Epoch 450/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.2990 - mean_absolute_error: 0.3012 - val_loss: 14.2808 - val_mean_absolute_error: 3.0260 Epoch 451/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.3138 - mean_absolute_error: 0.2885 - val_loss: 13.5403 - val_mean_absolute_error: 2.9229 Epoch 452/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.3289 - mean_absolute_error: 0.3041 - val_loss: 13.9211 - val_mean_absolute_error: 2.9321 Epoch 453/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.9625 - mean_absolute_error: 0.4619 - val_loss: 13.9263 - val_mean_absolute_error: 3.0322 Epoch 454/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.1198 - mean_absolute_error: 0.6691 - val_loss: 13.7401 - val_mean_absolute_error: 2.8305 Epoch 455/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.0061 - mean_absolute_error: 0.6357 - val_loss: 13.5849 - val_mean_absolute_error: 2.8604 Epoch 456/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.9038 - mean_absolute_error: 0.5877 - val_loss: 13.9312 - val_mean_absolute_error: 2.9396 Epoch 457/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.7801 - mean_absolute_error: 0.5672 - val_loss: 13.4106 - val_mean_absolute_error: 2.8520 Epoch 458/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.9256 - mean_absolute_error: 0.5766 - val_loss: 14.4511 - val_mean_absolute_error: 3.0389 Epoch 459/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.5129 - mean_absolute_error: 0.6662 - val_loss: 13.5682 - val_mean_absolute_error: 2.8899 Epoch 460/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.5448 - mean_absolute_error: 0.6652 - val_loss: 13.3512 - val_mean_absolute_error: 2.7714 Epoch 461/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.4427 - mean_absolute_error: 0.7378 - val_loss: 15.1325 - val_mean_absolute_error: 3.0471 Epoch 462/1000 7/7 [==============================] - 0s 21ms/step - loss: 2.4148 - mean_absolute_error: 0.8986 - val_loss: 14.1315 - val_mean_absolute_error: 2.7851 Epoch 463/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.3699 - mean_absolute_error: 0.7764 - val_loss: 13.8955 - val_mean_absolute_error: 3.0007 Epoch 464/1000 7/7 [==============================] - 0s 23ms/step - loss: 1.1255 - mean_absolute_error: 0.6788 - val_loss: 13.6991 - val_mean_absolute_error: 2.9719 Epoch 465/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.0048 - mean_absolute_error: 0.6606 - val_loss: 13.4004 - val_mean_absolute_error: 2.7704 Epoch 466/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.0692 - mean_absolute_error: 0.6740 - val_loss: 12.8422 - val_mean_absolute_error: 2.7991 Epoch 467/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.2775 - mean_absolute_error: 0.6779 - val_loss: 13.5324 - val_mean_absolute_error: 2.7083 Epoch 468/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.4483 - mean_absolute_error: 0.6742 - val_loss: 13.4707 - val_mean_absolute_error: 2.7837 Epoch 469/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.2464 - mean_absolute_error: 0.6525 - val_loss: 13.9963 - val_mean_absolute_error: 2.8792 Epoch 470/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.0505 - mean_absolute_error: 0.6492 - val_loss: 13.6430 - val_mean_absolute_error: 2.7537 Epoch 471/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.7727 - mean_absolute_error: 0.5023 - val_loss: 13.6524 - val_mean_absolute_error: 2.9402 Epoch 472/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.8393 - mean_absolute_error: 0.5149 - val_loss: 13.6786 - val_mean_absolute_error: 2.9190 Epoch 473/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.6637 - mean_absolute_error: 0.4600 - val_loss: 13.8980 - val_mean_absolute_error: 2.8058 Epoch 474/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.6534 - mean_absolute_error: 0.4619 - val_loss: 13.1283 - val_mean_absolute_error: 2.8004 Epoch 475/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.7250 - mean_absolute_error: 0.5022 - val_loss: 13.3989 - val_mean_absolute_error: 2.8392 Epoch 476/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.6747 - mean_absolute_error: 0.4348 - val_loss: 13.4179 - val_mean_absolute_error: 2.8171 Epoch 477/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.8883 - mean_absolute_error: 0.5171 - val_loss: 13.1078 - val_mean_absolute_error: 2.8162 Epoch 478/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.7234 - mean_absolute_error: 0.4810 - val_loss: 12.6723 - val_mean_absolute_error: 2.7590 Epoch 479/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.6337 - mean_absolute_error: 0.4518 - val_loss: 13.9750 - val_mean_absolute_error: 2.8389 Epoch 480/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5588 - mean_absolute_error: 0.4518 - val_loss: 13.6585 - val_mean_absolute_error: 2.8787 Epoch 481/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.6642 - mean_absolute_error: 0.4668 - val_loss: 14.5275 - val_mean_absolute_error: 3.0041 Epoch 482/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.9953 - mean_absolute_error: 0.5912 - val_loss: 14.6007 - val_mean_absolute_error: 2.8212 Epoch 483/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.7996 - mean_absolute_error: 0.6318 - val_loss: 12.7941 - val_mean_absolute_error: 2.7623 Epoch 484/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.8197 - mean_absolute_error: 0.6204 - val_loss: 15.1212 - val_mean_absolute_error: 3.0780 Epoch 485/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.7885 - mean_absolute_error: 0.5810 - val_loss: 14.0064 - val_mean_absolute_error: 2.8798 Epoch 486/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5289 - mean_absolute_error: 0.4575 - val_loss: 13.2925 - val_mean_absolute_error: 2.8543 Epoch 487/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.6550 - mean_absolute_error: 0.4870 - val_loss: 12.9255 - val_mean_absolute_error: 2.8578 Epoch 488/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.5139 - mean_absolute_error: 0.4094 - val_loss: 13.4765 - val_mean_absolute_error: 2.8459 Epoch 489/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.4060 - mean_absolute_error: 0.3640 - val_loss: 12.8902 - val_mean_absolute_error: 2.7824 Epoch 490/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.4335 - mean_absolute_error: 0.3990 - val_loss: 13.5204 - val_mean_absolute_error: 2.8949 Epoch 491/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4396 - mean_absolute_error: 0.3688 - val_loss: 13.2686 - val_mean_absolute_error: 2.8326 Epoch 492/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5094 - mean_absolute_error: 0.3887 - val_loss: 13.0784 - val_mean_absolute_error: 2.7847 Epoch 493/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4684 - mean_absolute_error: 0.3709 - val_loss: 13.6174 - val_mean_absolute_error: 2.8816 Epoch 494/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.4262 - mean_absolute_error: 0.3972 - val_loss: 13.7537 - val_mean_absolute_error: 2.9284 Epoch 495/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.4013 - mean_absolute_error: 0.3646 - val_loss: 12.9363 - val_mean_absolute_error: 2.7456 Epoch 496/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4150 - mean_absolute_error: 0.3992 - val_loss: 13.9029 - val_mean_absolute_error: 2.9410 Epoch 497/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.3313 - mean_absolute_error: 0.3808 - val_loss: 13.4273 - val_mean_absolute_error: 2.8710 Epoch 498/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3063 - mean_absolute_error: 0.3330 - val_loss: 13.2896 - val_mean_absolute_error: 2.8436 Epoch 499/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2799 - mean_absolute_error: 0.3309 - val_loss: 13.9772 - val_mean_absolute_error: 2.9364 Epoch 500/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.2855 - mean_absolute_error: 0.3196 - val_loss: 13.1030 - val_mean_absolute_error: 2.7856 Epoch 501/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2725 - mean_absolute_error: 0.3164 - val_loss: 14.0188 - val_mean_absolute_error: 2.9430 Epoch 502/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4399 - mean_absolute_error: 0.3466 - val_loss: 14.0528 - val_mean_absolute_error: 2.9083 Epoch 503/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.3846 - mean_absolute_error: 0.3737 - val_loss: 13.3117 - val_mean_absolute_error: 2.7880 Epoch 504/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2916 - mean_absolute_error: 0.3360 - val_loss: 13.8255 - val_mean_absolute_error: 2.9138 Epoch 505/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3098 - mean_absolute_error: 0.3563 - val_loss: 13.8587 - val_mean_absolute_error: 2.8655 Epoch 506/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2686 - mean_absolute_error: 0.3387 - val_loss: 13.0770 - val_mean_absolute_error: 2.8134 Epoch 507/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2280 - mean_absolute_error: 0.2980 - val_loss: 13.3853 - val_mean_absolute_error: 2.8154 Epoch 508/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2466 - mean_absolute_error: 0.3159 - val_loss: 13.8036 - val_mean_absolute_error: 2.9019 Epoch 509/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.3174 - mean_absolute_error: 0.3172 - val_loss: 13.3536 - val_mean_absolute_error: 2.7987 Epoch 510/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2007 - mean_absolute_error: 0.2889 - val_loss: 13.5399 - val_mean_absolute_error: 2.8462 Epoch 511/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2230 - mean_absolute_error: 0.2916 - val_loss: 13.4673 - val_mean_absolute_error: 2.8270 Epoch 512/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1856 - mean_absolute_error: 0.2512 - val_loss: 13.5554 - val_mean_absolute_error: 2.8709 Epoch 513/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1885 - mean_absolute_error: 0.2346 - val_loss: 13.2373 - val_mean_absolute_error: 2.7656 Epoch 514/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.2982 - mean_absolute_error: 0.2934 - val_loss: 13.7629 - val_mean_absolute_error: 2.9334 Epoch 515/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.3354 - mean_absolute_error: 0.3741 - val_loss: 13.4249 - val_mean_absolute_error: 2.8261 Epoch 516/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2000 - mean_absolute_error: 0.2969 - val_loss: 12.9408 - val_mean_absolute_error: 2.7939 Epoch 517/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3797 - mean_absolute_error: 0.3391 - val_loss: 13.6104 - val_mean_absolute_error: 2.8716 Epoch 518/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.2902 - mean_absolute_error: 0.3274 - val_loss: 13.5852 - val_mean_absolute_error: 2.8660 Epoch 519/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2429 - mean_absolute_error: 0.3200 - val_loss: 12.8229 - val_mean_absolute_error: 2.7857 Epoch 520/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1935 - mean_absolute_error: 0.2535 - val_loss: 13.6991 - val_mean_absolute_error: 2.9427 Epoch 521/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1896 - mean_absolute_error: 0.2770 - val_loss: 13.4141 - val_mean_absolute_error: 2.8294 Epoch 522/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.2368 - mean_absolute_error: 0.2893 - val_loss: 13.0254 - val_mean_absolute_error: 2.8138 Epoch 523/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.3257 - mean_absolute_error: 0.3143 - val_loss: 12.9925 - val_mean_absolute_error: 2.8348 Epoch 524/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2698 - mean_absolute_error: 0.3159 - val_loss: 13.7805 - val_mean_absolute_error: 2.8529 Epoch 525/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.2574 - mean_absolute_error: 0.3090 - val_loss: 13.3376 - val_mean_absolute_error: 2.8735 Epoch 526/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1819 - mean_absolute_error: 0.2725 - val_loss: 13.1385 - val_mean_absolute_error: 2.8149 Epoch 527/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1870 - mean_absolute_error: 0.2695 - val_loss: 13.6056 - val_mean_absolute_error: 2.8989 Epoch 528/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.1766 - mean_absolute_error: 0.2444 - val_loss: 13.2049 - val_mean_absolute_error: 2.8265 Epoch 529/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1621 - mean_absolute_error: 0.2409 - val_loss: 13.0746 - val_mean_absolute_error: 2.8190 Epoch 530/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1992 - mean_absolute_error: 0.2805 - val_loss: 13.7411 - val_mean_absolute_error: 2.9401 Epoch 531/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2245 - mean_absolute_error: 0.3024 - val_loss: 13.6451 - val_mean_absolute_error: 2.8947 Epoch 532/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3343 - mean_absolute_error: 0.3046 - val_loss: 13.6957 - val_mean_absolute_error: 2.8091 Epoch 533/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.3788 - mean_absolute_error: 0.3724 - val_loss: 12.6701 - val_mean_absolute_error: 2.7869 Epoch 534/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2665 - mean_absolute_error: 0.3404 - val_loss: 13.6235 - val_mean_absolute_error: 2.8976 Epoch 535/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.2448 - mean_absolute_error: 0.3424 - val_loss: 13.3336 - val_mean_absolute_error: 2.8345 Epoch 536/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1824 - mean_absolute_error: 0.2541 - val_loss: 12.8287 - val_mean_absolute_error: 2.7710 Epoch 537/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1608 - mean_absolute_error: 0.2458 - val_loss: 13.4785 - val_mean_absolute_error: 2.8844 Epoch 538/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2285 - mean_absolute_error: 0.2760 - val_loss: 13.3629 - val_mean_absolute_error: 2.8026 Epoch 539/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2822 - mean_absolute_error: 0.2948 - val_loss: 13.1528 - val_mean_absolute_error: 2.7412 Epoch 540/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.3876 - mean_absolute_error: 0.3431 - val_loss: 13.1033 - val_mean_absolute_error: 2.7873 Epoch 541/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3246 - mean_absolute_error: 0.3429 - val_loss: 13.2865 - val_mean_absolute_error: 2.8243 Epoch 542/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2625 - mean_absolute_error: 0.3274 - val_loss: 13.5174 - val_mean_absolute_error: 2.8326 Epoch 543/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2322 - mean_absolute_error: 0.3074 - val_loss: 13.4542 - val_mean_absolute_error: 2.8925 Epoch 544/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1785 - mean_absolute_error: 0.2591 - val_loss: 13.3131 - val_mean_absolute_error: 2.7785 Epoch 545/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1922 - mean_absolute_error: 0.2710 - val_loss: 12.7267 - val_mean_absolute_error: 2.7568 Epoch 546/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1721 - mean_absolute_error: 0.2492 - val_loss: 13.6111 - val_mean_absolute_error: 2.9058 Epoch 547/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1862 - mean_absolute_error: 0.2754 - val_loss: 12.8712 - val_mean_absolute_error: 2.7893 Epoch 548/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1972 - mean_absolute_error: 0.2405 - val_loss: 13.3051 - val_mean_absolute_error: 2.8470 Epoch 549/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.7661 - mean_absolute_error: 0.4052 - val_loss: 13.9286 - val_mean_absolute_error: 2.7932 Epoch 550/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.5331 - mean_absolute_error: 0.4749 - val_loss: 12.4985 - val_mean_absolute_error: 2.7828 Epoch 551/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3779 - mean_absolute_error: 0.4186 - val_loss: 13.0363 - val_mean_absolute_error: 2.8741 Epoch 552/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3506 - mean_absolute_error: 0.3902 - val_loss: 12.7053 - val_mean_absolute_error: 2.8297 Epoch 553/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3345 - mean_absolute_error: 0.3782 - val_loss: 13.0097 - val_mean_absolute_error: 2.8610 Epoch 554/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2551 - mean_absolute_error: 0.3404 - val_loss: 13.5527 - val_mean_absolute_error: 2.8888 Epoch 555/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.2593 - mean_absolute_error: 0.3340 - val_loss: 13.3368 - val_mean_absolute_error: 2.8975 Epoch 556/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1997 - mean_absolute_error: 0.2849 - val_loss: 12.9478 - val_mean_absolute_error: 2.8213 Epoch 557/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1624 - mean_absolute_error: 0.2604 - val_loss: 12.8950 - val_mean_absolute_error: 2.7986 Epoch 558/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2633 - mean_absolute_error: 0.2784 - val_loss: 13.6323 - val_mean_absolute_error: 2.9201 Epoch 559/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2048 - mean_absolute_error: 0.2928 - val_loss: 12.6261 - val_mean_absolute_error: 2.7828 Epoch 560/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1480 - mean_absolute_error: 0.2472 - val_loss: 13.0144 - val_mean_absolute_error: 2.8075 Epoch 561/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1552 - mean_absolute_error: 0.2378 - val_loss: 13.2516 - val_mean_absolute_error: 2.8548 Epoch 562/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1234 - mean_absolute_error: 0.2053 - val_loss: 13.0763 - val_mean_absolute_error: 2.8834 Epoch 563/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1214 - mean_absolute_error: 0.1908 - val_loss: 13.1710 - val_mean_absolute_error: 2.8365 Epoch 564/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1289 - mean_absolute_error: 0.1864 - val_loss: 13.2444 - val_mean_absolute_error: 2.8501 Epoch 565/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1068 - mean_absolute_error: 0.1551 - val_loss: 13.1563 - val_mean_absolute_error: 2.8476 Epoch 566/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1301 - mean_absolute_error: 0.1760 - val_loss: 13.1512 - val_mean_absolute_error: 2.8680 Epoch 567/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1471 - mean_absolute_error: 0.1999 - val_loss: 13.3427 - val_mean_absolute_error: 2.8590 Epoch 568/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2647 - mean_absolute_error: 0.2583 - val_loss: 13.5693 - val_mean_absolute_error: 2.8943 Epoch 569/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2024 - mean_absolute_error: 0.2927 - val_loss: 13.6833 - val_mean_absolute_error: 2.9474 Epoch 570/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.2106 - mean_absolute_error: 0.3130 - val_loss: 13.3581 - val_mean_absolute_error: 2.8687 Epoch 571/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1525 - mean_absolute_error: 0.2302 - val_loss: 13.6374 - val_mean_absolute_error: 2.8922 Epoch 572/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1508 - mean_absolute_error: 0.2357 - val_loss: 13.3458 - val_mean_absolute_error: 2.8461 Epoch 573/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.1192 - mean_absolute_error: 0.1999 - val_loss: 13.5945 - val_mean_absolute_error: 2.9435 Epoch 574/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.1267 - mean_absolute_error: 0.2144 - val_loss: 13.3606 - val_mean_absolute_error: 2.8644 Epoch 575/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1116 - mean_absolute_error: 0.1788 - val_loss: 13.2824 - val_mean_absolute_error: 2.8586 Epoch 576/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1138 - mean_absolute_error: 0.1819 - val_loss: 13.1591 - val_mean_absolute_error: 2.8366 Epoch 577/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1123 - mean_absolute_error: 0.1884 - val_loss: 13.4510 - val_mean_absolute_error: 2.9106 Epoch 578/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.1346 - mean_absolute_error: 0.2111 - val_loss: 13.2146 - val_mean_absolute_error: 2.8403 Epoch 579/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1208 - mean_absolute_error: 0.1902 - val_loss: 13.2890 - val_mean_absolute_error: 2.8573 Epoch 580/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1333 - mean_absolute_error: 0.2057 - val_loss: 13.4400 - val_mean_absolute_error: 2.8822 Epoch 581/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1717 - mean_absolute_error: 0.2050 - val_loss: 13.2070 - val_mean_absolute_error: 2.8630 Epoch 582/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1150 - mean_absolute_error: 0.1815 - val_loss: 13.4633 - val_mean_absolute_error: 2.8889 Epoch 583/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1245 - mean_absolute_error: 0.2021 - val_loss: 13.2697 - val_mean_absolute_error: 2.8273 Epoch 584/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.1290 - mean_absolute_error: 0.1938 - val_loss: 13.7150 - val_mean_absolute_error: 2.9140 Epoch 585/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.1311 - mean_absolute_error: 0.2143 - val_loss: 13.5167 - val_mean_absolute_error: 2.8719 Epoch 586/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.2192 - mean_absolute_error: 0.2677 - val_loss: 13.5373 - val_mean_absolute_error: 2.8997 Epoch 587/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.2519 - mean_absolute_error: 0.2973 - val_loss: 12.8709 - val_mean_absolute_error: 2.8006 Epoch 588/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.2240 - mean_absolute_error: 0.3227 - val_loss: 13.9189 - val_mean_absolute_error: 2.9682 Epoch 589/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.2964 - mean_absolute_error: 0.3803 - val_loss: 13.6907 - val_mean_absolute_error: 2.8981 Epoch 590/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.2662 - mean_absolute_error: 0.3442 - val_loss: 12.6306 - val_mean_absolute_error: 2.7732 Epoch 591/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.3651 - mean_absolute_error: 0.3967 - val_loss: 13.4874 - val_mean_absolute_error: 2.8437 Epoch 592/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2131 - mean_absolute_error: 0.3317 - val_loss: 13.8028 - val_mean_absolute_error: 2.8976 Epoch 593/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3310 - mean_absolute_error: 0.3477 - val_loss: 13.5262 - val_mean_absolute_error: 2.9016 Epoch 594/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2514 - mean_absolute_error: 0.3417 - val_loss: 12.9908 - val_mean_absolute_error: 2.7785 Epoch 595/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2283 - mean_absolute_error: 0.3287 - val_loss: 13.2383 - val_mean_absolute_error: 2.8624 Epoch 596/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1961 - mean_absolute_error: 0.2896 - val_loss: 12.9689 - val_mean_absolute_error: 2.8502 Epoch 597/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1995 - mean_absolute_error: 0.2845 - val_loss: 13.3882 - val_mean_absolute_error: 2.9036 Epoch 598/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2053 - mean_absolute_error: 0.2744 - val_loss: 13.4927 - val_mean_absolute_error: 2.8562 Epoch 599/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2179 - mean_absolute_error: 0.3089 - val_loss: 13.3133 - val_mean_absolute_error: 2.8424 Epoch 600/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1956 - mean_absolute_error: 0.3060 - val_loss: 13.5312 - val_mean_absolute_error: 2.8950 Epoch 601/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1607 - mean_absolute_error: 0.2453 - val_loss: 13.6336 - val_mean_absolute_error: 2.9017 Epoch 602/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2083 - mean_absolute_error: 0.2840 - val_loss: 13.0905 - val_mean_absolute_error: 2.8434 Epoch 603/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3272 - mean_absolute_error: 0.3104 - val_loss: 13.0850 - val_mean_absolute_error: 2.8776 Epoch 604/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.2024 - mean_absolute_error: 0.2811 - val_loss: 13.0505 - val_mean_absolute_error: 2.8625 Epoch 605/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2376 - mean_absolute_error: 0.2918 - val_loss: 13.3505 - val_mean_absolute_error: 2.8988 Epoch 606/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2268 - mean_absolute_error: 0.2834 - val_loss: 13.0473 - val_mean_absolute_error: 2.8520 Epoch 607/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1902 - mean_absolute_error: 0.2579 - val_loss: 13.4770 - val_mean_absolute_error: 2.9174 Epoch 608/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5006 - mean_absolute_error: 0.3074 - val_loss: 12.8826 - val_mean_absolute_error: 2.8101 Epoch 609/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.4399 - mean_absolute_error: 0.4044 - val_loss: 13.0814 - val_mean_absolute_error: 2.9117 Epoch 610/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.2394 - mean_absolute_error: 0.6597 - val_loss: 14.0580 - val_mean_absolute_error: 2.9346 Epoch 611/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.1122 - mean_absolute_error: 0.6137 - val_loss: 12.6906 - val_mean_absolute_error: 2.7956 Epoch 612/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.8638 - mean_absolute_error: 0.6189 - val_loss: 12.5440 - val_mean_absolute_error: 2.6015 Epoch 613/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.4981 - mean_absolute_error: 0.5079 - val_loss: 13.7693 - val_mean_absolute_error: 3.0086 Epoch 614/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.7176 - mean_absolute_error: 0.5401 - val_loss: 12.9129 - val_mean_absolute_error: 2.8637 Epoch 615/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.2386 - mean_absolute_error: 0.5901 - val_loss: 13.3238 - val_mean_absolute_error: 2.9013 Epoch 616/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.6534 - mean_absolute_error: 0.5985 - val_loss: 12.8090 - val_mean_absolute_error: 2.8180 Epoch 617/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4193 - mean_absolute_error: 0.4498 - val_loss: 13.9525 - val_mean_absolute_error: 2.9285 Epoch 618/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4582 - mean_absolute_error: 0.4528 - val_loss: 12.7366 - val_mean_absolute_error: 2.7974 Epoch 619/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.3216 - mean_absolute_error: 0.3831 - val_loss: 12.5660 - val_mean_absolute_error: 2.6392 Epoch 620/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.3511 - mean_absolute_error: 0.3786 - val_loss: 13.6184 - 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loss: 0.9324 - mean_absolute_error: 0.6888 - val_loss: 11.8571 - val_mean_absolute_error: 2.6709 Epoch 634/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.5717 - mean_absolute_error: 0.5515 - val_loss: 12.6594 - val_mean_absolute_error: 2.9007 Epoch 635/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.7710 - mean_absolute_error: 0.6507 - val_loss: 11.7763 - val_mean_absolute_error: 2.7337 Epoch 636/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.7046 - mean_absolute_error: 0.5912 - val_loss: 12.4490 - val_mean_absolute_error: 2.7305 Epoch 637/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.9119 - mean_absolute_error: 0.5552 - val_loss: 13.1661 - val_mean_absolute_error: 2.8617 Epoch 638/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.6068 - mean_absolute_error: 0.5850 - val_loss: 12.9403 - val_mean_absolute_error: 2.6694 Epoch 639/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.6167 - mean_absolute_error: 0.5305 - val_loss: 12.9742 - val_mean_absolute_error: 2.8822 Epoch 640/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3516 - mean_absolute_error: 0.4287 - val_loss: 12.4293 - val_mean_absolute_error: 2.7591 Epoch 641/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.3724 - mean_absolute_error: 0.4322 - val_loss: 12.0089 - val_mean_absolute_error: 2.7017 Epoch 642/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.4297 - mean_absolute_error: 0.4244 - val_loss: 12.8968 - val_mean_absolute_error: 2.7654 Epoch 643/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2396 - mean_absolute_error: 0.3395 - val_loss: 12.3524 - val_mean_absolute_error: 2.7300 Epoch 644/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2006 - mean_absolute_error: 0.2907 - val_loss: 12.2105 - val_mean_absolute_error: 2.6710 Epoch 645/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1553 - mean_absolute_error: 0.2358 - val_loss: 12.3096 - val_mean_absolute_error: 2.6893 Epoch 646/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1745 - mean_absolute_error: 0.2579 - val_loss: 12.8198 - val_mean_absolute_error: 2.8115 Epoch 647/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2840 - mean_absolute_error: 0.2784 - val_loss: 12.0410 - val_mean_absolute_error: 2.7129 Epoch 648/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3425 - mean_absolute_error: 0.3136 - val_loss: 12.2375 - val_mean_absolute_error: 2.6802 Epoch 649/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2543 - mean_absolute_error: 0.2780 - val_loss: 12.2733 - val_mean_absolute_error: 2.7417 Epoch 650/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1501 - mean_absolute_error: 0.2353 - val_loss: 12.1072 - val_mean_absolute_error: 2.7088 Epoch 651/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1243 - mean_absolute_error: 0.2163 - val_loss: 11.8400 - val_mean_absolute_error: 2.6929 Epoch 652/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1623 - mean_absolute_error: 0.2346 - val_loss: 12.4446 - val_mean_absolute_error: 2.7581 Epoch 653/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.2733 - mean_absolute_error: 0.2815 - val_loss: 12.2783 - val_mean_absolute_error: 2.6976 Epoch 654/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.3494 - mean_absolute_error: 0.3678 - val_loss: 11.6442 - val_mean_absolute_error: 2.6893 Epoch 655/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2041 - mean_absolute_error: 0.3014 - val_loss: 12.1984 - val_mean_absolute_error: 2.7337 Epoch 656/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2441 - mean_absolute_error: 0.3032 - val_loss: 12.3574 - val_mean_absolute_error: 2.7595 Epoch 657/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2139 - mean_absolute_error: 0.3017 - val_loss: 12.1956 - val_mean_absolute_error: 2.6698 Epoch 658/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1866 - mean_absolute_error: 0.2588 - val_loss: 12.4755 - val_mean_absolute_error: 2.7933 Epoch 659/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5798 - mean_absolute_error: 0.4022 - val_loss: 12.9489 - val_mean_absolute_error: 2.7484 Epoch 660/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5540 - mean_absolute_error: 0.4464 - val_loss: 12.0505 - val_mean_absolute_error: 2.7761 Epoch 661/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.4890 - mean_absolute_error: 0.4370 - val_loss: 12.0150 - val_mean_absolute_error: 2.6358 Epoch 662/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.4522 - mean_absolute_error: 0.4556 - val_loss: 12.9469 - val_mean_absolute_error: 2.8029 Epoch 663/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.5157 - mean_absolute_error: 0.4609 - val_loss: 12.5574 - val_mean_absolute_error: 2.8083 Epoch 664/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.8589 - mean_absolute_error: 0.4827 - val_loss: 12.1941 - val_mean_absolute_error: 2.7637 Epoch 665/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.7757 - mean_absolute_error: 0.4786 - val_loss: 12.0186 - val_mean_absolute_error: 2.6701 Epoch 666/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4144 - mean_absolute_error: 0.3967 - val_loss: 11.8065 - val_mean_absolute_error: 2.7315 Epoch 667/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5185 - mean_absolute_error: 0.4012 - val_loss: 12.1515 - val_mean_absolute_error: 2.7211 Epoch 668/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.4876 - mean_absolute_error: 0.3879 - val_loss: 12.4548 - val_mean_absolute_error: 2.8448 Epoch 669/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2313 - mean_absolute_error: 0.3153 - val_loss: 12.2637 - val_mean_absolute_error: 2.7582 Epoch 670/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.3173 - mean_absolute_error: 0.3102 - val_loss: 11.9896 - val_mean_absolute_error: 2.7798 Epoch 671/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2216 - mean_absolute_error: 0.3155 - val_loss: 12.2670 - val_mean_absolute_error: 2.7787 Epoch 672/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1816 - mean_absolute_error: 0.2812 - val_loss: 12.2173 - val_mean_absolute_error: 2.7886 Epoch 673/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2045 - mean_absolute_error: 0.2814 - val_loss: 11.4423 - val_mean_absolute_error: 2.6540 Epoch 674/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.5847 - mean_absolute_error: 0.3098 - val_loss: 11.8195 - val_mean_absolute_error: 2.7296 Epoch 675/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.3165 - mean_absolute_error: 0.2982 - val_loss: 12.4498 - val_mean_absolute_error: 2.7676 Epoch 676/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.3405 - mean_absolute_error: 0.3367 - val_loss: 11.9110 - val_mean_absolute_error: 2.7346 Epoch 677/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2963 - mean_absolute_error: 0.3214 - val_loss: 11.4614 - val_mean_absolute_error: 2.6880 Epoch 678/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.2244 - mean_absolute_error: 0.3039 - val_loss: 11.8654 - val_mean_absolute_error: 2.6873 Epoch 679/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.1748 - mean_absolute_error: 0.2607 - val_loss: 11.6102 - val_mean_absolute_error: 2.7073 Epoch 680/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.1837 - mean_absolute_error: 0.2729 - val_loss: 11.5818 - val_mean_absolute_error: 2.6577 Epoch 681/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.1887 - mean_absolute_error: 0.2887 - val_loss: 11.5256 - val_mean_absolute_error: 2.7013 Epoch 682/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1520 - mean_absolute_error: 0.2479 - val_loss: 11.8201 - val_mean_absolute_error: 2.7091 Epoch 683/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.1376 - mean_absolute_error: 0.2353 - val_loss: 11.7590 - val_mean_absolute_error: 2.7335 Epoch 684/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1488 - mean_absolute_error: 0.2529 - val_loss: 12.2691 - val_mean_absolute_error: 2.7577 Epoch 685/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1915 - mean_absolute_error: 0.2732 - val_loss: 11.8043 - val_mean_absolute_error: 2.6870 Epoch 686/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1749 - mean_absolute_error: 0.2646 - val_loss: 12.3878 - val_mean_absolute_error: 2.8576 Epoch 687/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.2301 - mean_absolute_error: 0.3059 - val_loss: 12.1442 - val_mean_absolute_error: 2.7079 Epoch 688/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.2263 - mean_absolute_error: 0.3172 - val_loss: 12.0225 - val_mean_absolute_error: 2.7631 Epoch 689/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3680 - mean_absolute_error: 0.4208 - val_loss: 12.2779 - val_mean_absolute_error: 2.7726 Epoch 690/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3534 - mean_absolute_error: 0.4163 - val_loss: 11.9325 - val_mean_absolute_error: 2.8562 Epoch 691/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3618 - mean_absolute_error: 0.4171 - val_loss: 11.8319 - val_mean_absolute_error: 2.7052 Epoch 692/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2508 - mean_absolute_error: 0.3367 - val_loss: 11.9145 - val_mean_absolute_error: 2.7610 Epoch 693/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1876 - mean_absolute_error: 0.2889 - val_loss: 11.3701 - val_mean_absolute_error: 2.6926 Epoch 694/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1715 - mean_absolute_error: 0.2916 - val_loss: 11.8786 - val_mean_absolute_error: 2.6923 Epoch 695/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2126 - mean_absolute_error: 0.2668 - val_loss: 11.7010 - val_mean_absolute_error: 2.7227 Epoch 696/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1806 - mean_absolute_error: 0.2805 - val_loss: 12.1548 - val_mean_absolute_error: 2.7810 Epoch 697/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1781 - mean_absolute_error: 0.2647 - val_loss: 11.6120 - val_mean_absolute_error: 2.7476 Epoch 698/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1543 - mean_absolute_error: 0.2514 - val_loss: 11.6332 - val_mean_absolute_error: 2.6927 Epoch 699/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1301 - mean_absolute_error: 0.2281 - val_loss: 11.5381 - val_mean_absolute_error: 2.7224 Epoch 700/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1389 - mean_absolute_error: 0.2125 - val_loss: 11.6653 - val_mean_absolute_error: 2.6981 Epoch 701/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1658 - mean_absolute_error: 0.2464 - val_loss: 11.7218 - val_mean_absolute_error: 2.7147 Epoch 702/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1730 - mean_absolute_error: 0.2534 - val_loss: 11.4552 - val_mean_absolute_error: 2.6704 Epoch 703/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1598 - mean_absolute_error: 0.2500 - val_loss: 12.1941 - val_mean_absolute_error: 2.7815 Epoch 704/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1655 - mean_absolute_error: 0.2545 - val_loss: 12.3536 - val_mean_absolute_error: 2.8016 Epoch 705/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1462 - mean_absolute_error: 0.2379 - val_loss: 11.9855 - val_mean_absolute_error: 2.7170 Epoch 706/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1123 - mean_absolute_error: 0.1964 - val_loss: 11.8417 - val_mean_absolute_error: 2.7128 Epoch 707/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1136 - mean_absolute_error: 0.2008 - val_loss: 11.8996 - val_mean_absolute_error: 2.7323 Epoch 708/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0942 - mean_absolute_error: 0.1668 - val_loss: 12.1136 - val_mean_absolute_error: 2.7832 Epoch 709/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1189 - mean_absolute_error: 0.2021 - val_loss: 11.4317 - val_mean_absolute_error: 2.6596 Epoch 710/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1128 - mean_absolute_error: 0.1973 - val_loss: 11.6737 - val_mean_absolute_error: 2.7408 Epoch 711/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1015 - mean_absolute_error: 0.1830 - val_loss: 11.9220 - val_mean_absolute_error: 2.7325 Epoch 712/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.0995 - mean_absolute_error: 0.1770 - val_loss: 11.6589 - val_mean_absolute_error: 2.7120 Epoch 713/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.1031 - mean_absolute_error: 0.1775 - val_loss: 11.8986 - val_mean_absolute_error: 2.7398 Epoch 714/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0961 - mean_absolute_error: 0.1537 - val_loss: 11.6518 - val_mean_absolute_error: 2.7314 Epoch 715/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0803 - mean_absolute_error: 0.1401 - val_loss: 11.7197 - val_mean_absolute_error: 2.7168 Epoch 716/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0788 - mean_absolute_error: 0.1250 - val_loss: 11.8253 - val_mean_absolute_error: 2.7264 Epoch 717/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0743 - mean_absolute_error: 0.1125 - val_loss: 11.8410 - val_mean_absolute_error: 2.7483 Epoch 718/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.0788 - mean_absolute_error: 0.1355 - val_loss: 11.7417 - val_mean_absolute_error: 2.7205 Epoch 719/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0811 - mean_absolute_error: 0.1346 - val_loss: 12.0186 - val_mean_absolute_error: 2.7726 Epoch 720/1000 7/7 [==============================] - 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loss: 0.1752 - mean_absolute_error: 0.3061 - val_loss: 12.0477 - val_mean_absolute_error: 2.7919 Epoch 727/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.1767 - mean_absolute_error: 0.2575 - val_loss: 11.9367 - val_mean_absolute_error: 2.7318 Epoch 728/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.1428 - mean_absolute_error: 0.2303 - val_loss: 11.6728 - val_mean_absolute_error: 2.7505 Epoch 729/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.1328 - mean_absolute_error: 0.2274 - val_loss: 11.5297 - val_mean_absolute_error: 2.6818 Epoch 730/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.1333 - mean_absolute_error: 0.2205 - val_loss: 11.8827 - val_mean_absolute_error: 2.7424 Epoch 731/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.1409 - mean_absolute_error: 0.2148 - val_loss: 11.6266 - val_mean_absolute_error: 2.7094 Epoch 732/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1283 - mean_absolute_error: 0.1953 - val_loss: 11.8602 - val_mean_absolute_error: 2.7609 Epoch 733/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1062 - mean_absolute_error: 0.1821 - val_loss: 11.8830 - val_mean_absolute_error: 2.7507 Epoch 734/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1119 - mean_absolute_error: 0.1819 - val_loss: 11.6271 - val_mean_absolute_error: 2.6857 Epoch 735/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0971 - mean_absolute_error: 0.1693 - val_loss: 11.9326 - val_mean_absolute_error: 2.7193 Epoch 736/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.0948 - mean_absolute_error: 0.1596 - val_loss: 11.7998 - val_mean_absolute_error: 2.7077 Epoch 737/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0871 - mean_absolute_error: 0.1451 - val_loss: 11.4200 - val_mean_absolute_error: 2.6882 Epoch 738/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1212 - mean_absolute_error: 0.1787 - 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loss: 0.0904 - mean_absolute_error: 0.1553 - val_loss: 11.7240 - val_mean_absolute_error: 2.7204 Epoch 758/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0971 - mean_absolute_error: 0.1457 - val_loss: 12.3583 - val_mean_absolute_error: 2.7925 Epoch 759/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1103 - mean_absolute_error: 0.1537 - val_loss: 11.9988 - val_mean_absolute_error: 2.7512 Epoch 760/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0918 - mean_absolute_error: 0.1436 - val_loss: 11.7432 - val_mean_absolute_error: 2.7250 Epoch 761/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1009 - mean_absolute_error: 0.1646 - val_loss: 11.6021 - val_mean_absolute_error: 2.6760 Epoch 762/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1074 - mean_absolute_error: 0.1663 - val_loss: 12.0018 - val_mean_absolute_error: 2.7352 Epoch 763/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0871 - mean_absolute_error: 0.1539 - val_loss: 11.7222 - val_mean_absolute_error: 2.7016 Epoch 764/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0872 - mean_absolute_error: 0.1522 - val_loss: 11.6489 - val_mean_absolute_error: 2.7139 Epoch 765/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0862 - mean_absolute_error: 0.1480 - val_loss: 11.7534 - val_mean_absolute_error: 2.6832 Epoch 766/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1023 - mean_absolute_error: 0.1679 - val_loss: 12.0155 - val_mean_absolute_error: 2.7441 Epoch 767/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1116 - mean_absolute_error: 0.2016 - val_loss: 11.6758 - val_mean_absolute_error: 2.7097 Epoch 768/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1433 - mean_absolute_error: 0.2052 - val_loss: 11.7611 - val_mean_absolute_error: 2.6864 Epoch 769/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1357 - mean_absolute_error: 0.2008 - val_loss: 11.7731 - val_mean_absolute_error: 2.6975 Epoch 770/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1082 - mean_absolute_error: 0.1828 - val_loss: 11.8797 - val_mean_absolute_error: 2.7169 Epoch 771/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0975 - mean_absolute_error: 0.1649 - val_loss: 11.9971 - val_mean_absolute_error: 2.7290 Epoch 772/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0980 - mean_absolute_error: 0.1695 - val_loss: 12.0701 - val_mean_absolute_error: 2.7749 Epoch 773/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1239 - mean_absolute_error: 0.1900 - val_loss: 11.5758 - val_mean_absolute_error: 2.6816 Epoch 774/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3032 - mean_absolute_error: 0.2931 - val_loss: 11.9959 - val_mean_absolute_error: 2.7522 Epoch 775/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.5485 - mean_absolute_error: 0.3756 - val_loss: 11.7380 - val_mean_absolute_error: 2.7340 Epoch 776/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1832 - mean_absolute_error: 0.3054 - val_loss: 11.1794 - val_mean_absolute_error: 2.5185 Epoch 777/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2214 - mean_absolute_error: 0.2693 - val_loss: 12.0110 - val_mean_absolute_error: 2.7313 Epoch 778/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2281 - mean_absolute_error: 0.2924 - val_loss: 11.5895 - val_mean_absolute_error: 2.6658 Epoch 779/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.6466 - mean_absolute_error: 0.4547 - val_loss: 12.4876 - val_mean_absolute_error: 2.8670 Epoch 780/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.4514 - mean_absolute_error: 0.4534 - val_loss: 12.6957 - val_mean_absolute_error: 2.8498 Epoch 781/1000 7/7 [==============================] - 0s 43ms/step - loss: 0.4414 - mean_absolute_error: 0.4412 - val_loss: 10.9248 - val_mean_absolute_error: 2.6706 Epoch 782/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3948 - mean_absolute_error: 0.4219 - val_loss: 11.4883 - val_mean_absolute_error: 2.6665 Epoch 783/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.4646 - mean_absolute_error: 0.4927 - val_loss: 11.7875 - val_mean_absolute_error: 2.6544 Epoch 784/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4200 - mean_absolute_error: 0.4563 - val_loss: 12.5631 - val_mean_absolute_error: 2.8973 Epoch 785/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.2499 - mean_absolute_error: 0.3551 - val_loss: 12.3716 - val_mean_absolute_error: 2.7311 Epoch 786/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2709 - mean_absolute_error: 0.3635 - val_loss: 11.8565 - val_mean_absolute_error: 2.7020 Epoch 787/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2928 - mean_absolute_error: 0.3688 - val_loss: 12.0482 - val_mean_absolute_error: 2.8045 Epoch 788/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3738 - mean_absolute_error: 0.4025 - val_loss: 12.2408 - val_mean_absolute_error: 2.7844 Epoch 789/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2440 - mean_absolute_error: 0.3502 - val_loss: 11.5000 - val_mean_absolute_error: 2.7260 Epoch 790/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1867 - mean_absolute_error: 0.2906 - val_loss: 11.6427 - val_mean_absolute_error: 2.7058 Epoch 791/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1620 - mean_absolute_error: 0.2567 - val_loss: 12.2835 - val_mean_absolute_error: 2.8486 Epoch 792/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2075 - mean_absolute_error: 0.2953 - val_loss: 12.2096 - val_mean_absolute_error: 2.7930 Epoch 793/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1510 - mean_absolute_error: 0.2539 - val_loss: 11.7802 - val_mean_absolute_error: 2.7412 Epoch 794/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1175 - mean_absolute_error: 0.1975 - val_loss: 11.9528 - val_mean_absolute_error: 2.7637 Epoch 795/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1155 - mean_absolute_error: 0.2043 - val_loss: 11.3747 - val_mean_absolute_error: 2.6927 Epoch 796/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1079 - mean_absolute_error: 0.2006 - val_loss: 11.7404 - val_mean_absolute_error: 2.7448 Epoch 797/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1207 - mean_absolute_error: 0.2146 - val_loss: 11.8261 - val_mean_absolute_error: 2.7702 Epoch 798/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1018 - mean_absolute_error: 0.1891 - val_loss: 11.5953 - val_mean_absolute_error: 2.7033 Epoch 799/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1011 - mean_absolute_error: 0.1784 - val_loss: 11.7294 - val_mean_absolute_error: 2.7315 Epoch 800/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0968 - mean_absolute_error: 0.1680 - val_loss: 11.5593 - val_mean_absolute_error: 2.6847 Epoch 801/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0932 - mean_absolute_error: 0.1652 - val_loss: 11.3256 - val_mean_absolute_error: 2.6769 Epoch 802/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0907 - mean_absolute_error: 0.1607 - val_loss: 11.5700 - val_mean_absolute_error: 2.7000 Epoch 803/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0889 - mean_absolute_error: 0.1553 - val_loss: 11.8075 - val_mean_absolute_error: 2.7738 Epoch 804/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0852 - mean_absolute_error: 0.1507 - val_loss: 11.4806 - val_mean_absolute_error: 2.6928 Epoch 805/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0858 - mean_absolute_error: 0.1423 - val_loss: 11.4929 - val_mean_absolute_error: 2.6863 Epoch 806/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1083 - mean_absolute_error: 0.1762 - val_loss: 11.8160 - val_mean_absolute_error: 2.7771 Epoch 807/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0942 - mean_absolute_error: 0.1632 - val_loss: 11.7538 - val_mean_absolute_error: 2.7182 Epoch 808/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0911 - mean_absolute_error: 0.1444 - val_loss: 11.6864 - val_mean_absolute_error: 2.6873 Epoch 809/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0908 - mean_absolute_error: 0.1445 - val_loss: 11.7477 - val_mean_absolute_error: 2.7365 Epoch 810/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0879 - mean_absolute_error: 0.1491 - val_loss: 11.8759 - val_mean_absolute_error: 2.7596 Epoch 811/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0854 - mean_absolute_error: 0.1363 - val_loss: 11.3779 - val_mean_absolute_error: 2.6864 Epoch 812/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0703 - mean_absolute_error: 0.1379 - val_loss: 11.4180 - val_mean_absolute_error: 2.6641 Epoch 813/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1538 - mean_absolute_error: 0.1543 - val_loss: 11.7217 - val_mean_absolute_error: 2.7357 Epoch 814/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1166 - mean_absolute_error: 0.1615 - val_loss: 11.6232 - val_mean_absolute_error: 2.7016 Epoch 815/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0942 - mean_absolute_error: 0.1706 - val_loss: 11.3200 - val_mean_absolute_error: 2.6407 Epoch 816/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2663 - mean_absolute_error: 0.1959 - val_loss: 11.7527 - val_mean_absolute_error: 2.7110 Epoch 817/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.2337 - mean_absolute_error: 0.2734 - val_loss: 11.9703 - val_mean_absolute_error: 2.7928 Epoch 818/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.2595 - mean_absolute_error: 0.2859 - val_loss: 11.3873 - val_mean_absolute_error: 2.6923 Epoch 819/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2906 - mean_absolute_error: 0.3652 - val_loss: 12.7168 - val_mean_absolute_error: 2.7942 Epoch 820/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.3030 - mean_absolute_error: 0.3818 - val_loss: 12.0828 - val_mean_absolute_error: 2.8195 Epoch 821/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3029 - mean_absolute_error: 0.3606 - val_loss: 12.2968 - val_mean_absolute_error: 2.8513 Epoch 822/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2775 - mean_absolute_error: 0.3605 - val_loss: 11.9972 - val_mean_absolute_error: 2.7796 Epoch 823/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2060 - mean_absolute_error: 0.2923 - val_loss: 11.6186 - val_mean_absolute_error: 2.7407 Epoch 824/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2326 - mean_absolute_error: 0.3348 - val_loss: 12.6261 - val_mean_absolute_error: 2.8159 Epoch 825/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2084 - mean_absolute_error: 0.2944 - val_loss: 12.1392 - val_mean_absolute_error: 2.7915 Epoch 826/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2926 - mean_absolute_error: 0.3466 - val_loss: 12.0025 - val_mean_absolute_error: 2.7241 Epoch 827/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2140 - mean_absolute_error: 0.3243 - val_loss: 12.2041 - val_mean_absolute_error: 2.7632 Epoch 828/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2398 - mean_absolute_error: 0.3228 - val_loss: 10.9210 - val_mean_absolute_error: 2.5942 Epoch 829/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2055 - mean_absolute_error: 0.3019 - val_loss: 11.4749 - val_mean_absolute_error: 2.6808 Epoch 830/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2071 - mean_absolute_error: 0.3145 - val_loss: 11.6667 - val_mean_absolute_error: 2.6814 Epoch 831/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1570 - mean_absolute_error: 0.2620 - val_loss: 12.1388 - val_mean_absolute_error: 2.8542 Epoch 832/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.1800 - mean_absolute_error: 0.2927 - val_loss: 11.7060 - val_mean_absolute_error: 2.6066 Epoch 833/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1960 - mean_absolute_error: 0.3046 - val_loss: 11.5922 - val_mean_absolute_error: 2.7404 Epoch 834/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1673 - mean_absolute_error: 0.2873 - val_loss: 11.1601 - val_mean_absolute_error: 2.6332 Epoch 835/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1370 - mean_absolute_error: 0.2356 - val_loss: 11.6773 - val_mean_absolute_error: 2.7463 Epoch 836/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1441 - mean_absolute_error: 0.2452 - val_loss: 11.9831 - val_mean_absolute_error: 2.7799 Epoch 837/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2597 - mean_absolute_error: 0.3221 - val_loss: 11.7070 - val_mean_absolute_error: 2.7814 Epoch 838/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.6480 - mean_absolute_error: 0.5027 - val_loss: 10.9174 - val_mean_absolute_error: 2.6252 Epoch 839/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.7726 - mean_absolute_error: 0.5586 - val_loss: 13.1511 - val_mean_absolute_error: 2.9712 Epoch 840/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.7652 - mean_absolute_error: 0.6567 - val_loss: 11.4179 - val_mean_absolute_error: 2.6414 Epoch 841/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.7790 - mean_absolute_error: 0.6483 - val_loss: 11.5637 - val_mean_absolute_error: 2.5780 Epoch 842/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.2285 - mean_absolute_error: 0.7608 - val_loss: 11.6899 - val_mean_absolute_error: 2.7881 Epoch 843/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.2613 - mean_absolute_error: 0.8221 - val_loss: 11.8847 - val_mean_absolute_error: 2.8106 Epoch 844/1000 7/7 [==============================] - 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loss: 2.6002 - mean_absolute_error: 1.0872 - val_loss: 12.8847 - val_mean_absolute_error: 2.7580 Epoch 851/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.9402 - mean_absolute_error: 0.9772 - val_loss: 10.9627 - val_mean_absolute_error: 2.4820 Epoch 852/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.4753 - mean_absolute_error: 0.9238 - val_loss: 11.0020 - val_mean_absolute_error: 2.3537 Epoch 853/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.0666 - mean_absolute_error: 0.8074 - val_loss: 14.0335 - val_mean_absolute_error: 2.8931 Epoch 854/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.3020 - mean_absolute_error: 0.7664 - val_loss: 10.5357 - val_mean_absolute_error: 2.3920 Epoch 855/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.7782 - mean_absolute_error: 0.6480 - val_loss: 11.9646 - val_mean_absolute_error: 2.7982 Epoch 856/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.5433 - mean_absolute_error: 0.5303 - val_loss: 11.5077 - val_mean_absolute_error: 2.6046 Epoch 857/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4949 - mean_absolute_error: 0.5165 - val_loss: 11.6285 - val_mean_absolute_error: 2.7369 Epoch 858/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4682 - mean_absolute_error: 0.4758 - val_loss: 11.9551 - val_mean_absolute_error: 2.6649 Epoch 859/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.4984 - mean_absolute_error: 0.4447 - val_loss: 13.7635 - val_mean_absolute_error: 2.8224 Epoch 860/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4281 - mean_absolute_error: 0.4095 - val_loss: 11.3838 - val_mean_absolute_error: 2.6373 Epoch 861/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3220 - mean_absolute_error: 0.3987 - val_loss: 11.9609 - val_mean_absolute_error: 2.7958 Epoch 862/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2188 - mean_absolute_error: 0.3105 - val_loss: 11.4656 - val_mean_absolute_error: 2.6061 Epoch 863/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2203 - mean_absolute_error: 0.3223 - val_loss: 11.8355 - val_mean_absolute_error: 2.6920 Epoch 864/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1652 - mean_absolute_error: 0.2703 - val_loss: 11.0199 - val_mean_absolute_error: 2.5864 Epoch 865/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1442 - mean_absolute_error: 0.2342 - val_loss: 11.4595 - val_mean_absolute_error: 2.6296 Epoch 866/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1363 - mean_absolute_error: 0.2221 - val_loss: 11.0560 - val_mean_absolute_error: 2.6027 Epoch 867/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.1212 - mean_absolute_error: 0.1971 - val_loss: 11.1946 - val_mean_absolute_error: 2.5886 Epoch 868/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.1135 - mean_absolute_error: 0.1942 - val_loss: 11.4531 - val_mean_absolute_error: 2.6415 Epoch 869/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.1039 - mean_absolute_error: 0.1749 - val_loss: 11.4406 - val_mean_absolute_error: 2.6381 Epoch 870/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.0900 - mean_absolute_error: 0.1533 - val_loss: 11.2964 - val_mean_absolute_error: 2.6392 Epoch 871/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.0902 - mean_absolute_error: 0.1494 - val_loss: 11.1860 - val_mean_absolute_error: 2.5987 Epoch 872/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.0884 - mean_absolute_error: 0.1444 - val_loss: 11.2208 - val_mean_absolute_error: 2.6116 Epoch 873/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.0922 - mean_absolute_error: 0.1457 - val_loss: 11.2350 - val_mean_absolute_error: 2.6099 Epoch 874/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1107 - mean_absolute_error: 0.1596 - val_loss: 11.1943 - val_mean_absolute_error: 2.6204 Epoch 875/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0997 - mean_absolute_error: 0.1618 - val_loss: 11.2595 - val_mean_absolute_error: 2.6082 Epoch 876/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0946 - mean_absolute_error: 0.1572 - val_loss: 11.2322 - val_mean_absolute_error: 2.6253 Epoch 877/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0863 - mean_absolute_error: 0.1384 - val_loss: 11.4147 - val_mean_absolute_error: 2.6357 Epoch 878/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0937 - mean_absolute_error: 0.1511 - val_loss: 11.1506 - val_mean_absolute_error: 2.6004 Epoch 879/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0941 - mean_absolute_error: 0.1561 - val_loss: 11.3427 - val_mean_absolute_error: 2.6211 Epoch 880/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0881 - mean_absolute_error: 0.1428 - val_loss: 11.1204 - val_mean_absolute_error: 2.5992 Epoch 881/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0846 - mean_absolute_error: 0.1413 - val_loss: 11.2999 - val_mean_absolute_error: 2.6273 Epoch 882/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0870 - mean_absolute_error: 0.1408 - val_loss: 11.1404 - val_mean_absolute_error: 2.6134 Epoch 883/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0811 - mean_absolute_error: 0.1370 - val_loss: 11.1944 - val_mean_absolute_error: 2.6101 Epoch 884/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0785 - mean_absolute_error: 0.1285 - val_loss: 11.2401 - val_mean_absolute_error: 2.6310 Epoch 885/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0862 - mean_absolute_error: 0.1407 - val_loss: 11.3461 - val_mean_absolute_error: 2.6313 Epoch 886/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.0840 - mean_absolute_error: 0.1380 - val_loss: 11.1891 - val_mean_absolute_error: 2.6169 Epoch 887/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.1035 - mean_absolute_error: 0.1595 - val_loss: 11.3696 - val_mean_absolute_error: 2.6307 Epoch 888/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.0851 - mean_absolute_error: 0.1571 - val_loss: 10.9426 - val_mean_absolute_error: 2.5619 Epoch 889/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.1604 - mean_absolute_error: 0.1857 - val_loss: 11.0259 - val_mean_absolute_error: 2.5606 Epoch 890/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0893 - mean_absolute_error: 0.1486 - val_loss: 11.2509 - val_mean_absolute_error: 2.6132 Epoch 891/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0861 - mean_absolute_error: 0.1529 - val_loss: 11.2617 - val_mean_absolute_error: 2.6195 Epoch 892/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0815 - mean_absolute_error: 0.1373 - val_loss: 11.0545 - val_mean_absolute_error: 2.6019 Epoch 893/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0734 - mean_absolute_error: 0.1095 - val_loss: 11.1757 - val_mean_absolute_error: 2.6073 Epoch 894/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.0761 - mean_absolute_error: 0.1138 - val_loss: 11.3148 - val_mean_absolute_error: 2.6099 Epoch 895/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0738 - mean_absolute_error: 0.1056 - val_loss: 11.2091 - val_mean_absolute_error: 2.6190 Epoch 896/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0814 - mean_absolute_error: 0.1165 - val_loss: 11.2372 - val_mean_absolute_error: 2.6121 Epoch 897/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0739 - mean_absolute_error: 0.1089 - val_loss: 11.2523 - val_mean_absolute_error: 2.6124 Epoch 898/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0811 - mean_absolute_error: 0.1200 - val_loss: 11.2460 - val_mean_absolute_error: 2.6144 Epoch 899/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.0871 - mean_absolute_error: 0.1334 - val_loss: 11.1357 - val_mean_absolute_error: 2.6105 Epoch 900/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0774 - mean_absolute_error: 0.1242 - val_loss: 11.1862 - val_mean_absolute_error: 2.6075 Epoch 901/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0744 - mean_absolute_error: 0.1220 - val_loss: 11.2842 - val_mean_absolute_error: 2.6405 Epoch 902/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0744 - mean_absolute_error: 0.1208 - val_loss: 11.1749 - val_mean_absolute_error: 2.6047 Epoch 903/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0930 - mean_absolute_error: 0.1314 - val_loss: 11.0562 - val_mean_absolute_error: 2.5937 Epoch 904/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0795 - mean_absolute_error: 0.1224 - val_loss: 11.3656 - val_mean_absolute_error: 2.6297 Epoch 905/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0928 - mean_absolute_error: 0.1243 - val_loss: 11.0786 - val_mean_absolute_error: 2.6372 Epoch 906/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1055 - mean_absolute_error: 0.1483 - val_loss: 11.1037 - val_mean_absolute_error: 2.6017 Epoch 907/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0835 - mean_absolute_error: 0.1214 - val_loss: 11.1164 - val_mean_absolute_error: 2.6098 Epoch 908/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1406 - mean_absolute_error: 0.1361 - val_loss: 11.3302 - val_mean_absolute_error: 2.6316 Epoch 909/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2578 - mean_absolute_error: 0.2525 - val_loss: 11.4789 - val_mean_absolute_error: 2.5833 Epoch 910/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4509 - mean_absolute_error: 0.3220 - val_loss: 10.9792 - val_mean_absolute_error: 2.6170 Epoch 911/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2597 - mean_absolute_error: 0.3257 - val_loss: 11.0506 - val_mean_absolute_error: 2.6042 Epoch 912/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2450 - mean_absolute_error: 0.3295 - val_loss: 11.4915 - val_mean_absolute_error: 2.6332 Epoch 913/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1820 - mean_absolute_error: 0.2833 - val_loss: 11.3997 - val_mean_absolute_error: 2.6809 Epoch 914/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1238 - mean_absolute_error: 0.2278 - val_loss: 11.1969 - val_mean_absolute_error: 2.6148 Epoch 915/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1339 - mean_absolute_error: 0.2142 - val_loss: 11.0669 - val_mean_absolute_error: 2.6243 Epoch 916/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1032 - mean_absolute_error: 0.1881 - val_loss: 11.3040 - val_mean_absolute_error: 2.6372 Epoch 917/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1055 - mean_absolute_error: 0.1708 - val_loss: 11.1392 - val_mean_absolute_error: 2.6269 Epoch 918/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0925 - mean_absolute_error: 0.1585 - val_loss: 11.3230 - val_mean_absolute_error: 2.6365 Epoch 919/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0900 - mean_absolute_error: 0.1434 - val_loss: 10.9745 - val_mean_absolute_error: 2.5956 Epoch 920/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0807 - mean_absolute_error: 0.1370 - val_loss: 11.1464 - val_mean_absolute_error: 2.6050 Epoch 921/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0860 - mean_absolute_error: 0.1394 - val_loss: 11.3811 - val_mean_absolute_error: 2.6818 Epoch 922/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0836 - mean_absolute_error: 0.1403 - val_loss: 11.2548 - val_mean_absolute_error: 2.6212 Epoch 923/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0776 - mean_absolute_error: 0.1223 - val_loss: 11.2110 - val_mean_absolute_error: 2.6217 Epoch 924/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0716 - mean_absolute_error: 0.1184 - val_loss: 11.2845 - val_mean_absolute_error: 2.6146 Epoch 925/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.0745 - mean_absolute_error: 0.1112 - val_loss: 11.0930 - val_mean_absolute_error: 2.6055 Epoch 926/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.0758 - mean_absolute_error: 0.1112 - val_loss: 11.0257 - val_mean_absolute_error: 2.6016 Epoch 927/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0765 - mean_absolute_error: 0.1195 - val_loss: 11.2188 - val_mean_absolute_error: 2.6421 Epoch 928/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0726 - mean_absolute_error: 0.1164 - val_loss: 11.2188 - val_mean_absolute_error: 2.6000 Epoch 929/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0803 - mean_absolute_error: 0.1230 - val_loss: 10.9848 - val_mean_absolute_error: 2.5988 Epoch 930/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0761 - mean_absolute_error: 0.1374 - val_loss: 11.3888 - val_mean_absolute_error: 2.6543 Epoch 931/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0784 - mean_absolute_error: 0.1359 - val_loss: 11.0000 - val_mean_absolute_error: 2.5834 Epoch 932/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0752 - mean_absolute_error: 0.1266 - val_loss: 11.0095 - val_mean_absolute_error: 2.5907 Epoch 933/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0773 - mean_absolute_error: 0.1205 - val_loss: 11.2841 - val_mean_absolute_error: 2.6455 Epoch 934/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0804 - mean_absolute_error: 0.1332 - val_loss: 11.0779 - val_mean_absolute_error: 2.6012 Epoch 935/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0739 - mean_absolute_error: 0.1220 - val_loss: 11.2427 - val_mean_absolute_error: 2.6035 Epoch 936/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0766 - mean_absolute_error: 0.1225 - val_loss: 11.1223 - val_mean_absolute_error: 2.6075 Epoch 937/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0741 - mean_absolute_error: 0.1126 - val_loss: 11.0944 - val_mean_absolute_error: 2.5910 Epoch 938/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0807 - mean_absolute_error: 0.1223 - val_loss: 11.1461 - val_mean_absolute_error: 2.6583 Epoch 939/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0896 - mean_absolute_error: 0.1395 - val_loss: 11.2037 - val_mean_absolute_error: 2.6040 Epoch 940/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0769 - mean_absolute_error: 0.1361 - val_loss: 11.1988 - val_mean_absolute_error: 2.6428 Epoch 941/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0829 - mean_absolute_error: 0.1308 - val_loss: 11.1132 - val_mean_absolute_error: 2.6004 Epoch 942/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0810 - mean_absolute_error: 0.1364 - val_loss: 11.1591 - val_mean_absolute_error: 2.6246 Epoch 943/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0764 - mean_absolute_error: 0.1277 - val_loss: 11.1029 - val_mean_absolute_error: 2.6043 Epoch 944/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0805 - mean_absolute_error: 0.1233 - val_loss: 11.3876 - val_mean_absolute_error: 2.6474 Epoch 945/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0832 - mean_absolute_error: 0.1300 - val_loss: 11.2524 - val_mean_absolute_error: 2.6385 Epoch 946/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0745 - mean_absolute_error: 0.1166 - val_loss: 11.3848 - val_mean_absolute_error: 2.6341 Epoch 947/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0755 - mean_absolute_error: 0.1360 - val_loss: 11.1582 - val_mean_absolute_error: 2.6179 Epoch 948/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0919 - mean_absolute_error: 0.1621 - val_loss: 11.2005 - val_mean_absolute_error: 2.6473 Epoch 949/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0779 - mean_absolute_error: 0.1376 - val_loss: 11.2634 - val_mean_absolute_error: 2.6352 Epoch 950/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0826 - mean_absolute_error: 0.1283 - val_loss: 11.2864 - val_mean_absolute_error: 2.6480 Epoch 951/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.0689 - mean_absolute_error: 0.1112 - val_loss: 11.1598 - val_mean_absolute_error: 2.6091 Epoch 952/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0766 - mean_absolute_error: 0.1116 - val_loss: 11.1621 - val_mean_absolute_error: 2.6049 Epoch 953/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0835 - mean_absolute_error: 0.1131 - val_loss: 11.2149 - val_mean_absolute_error: 2.6408 Epoch 954/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0802 - mean_absolute_error: 0.1219 - val_loss: 11.3457 - val_mean_absolute_error: 2.6284 Epoch 955/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0767 - mean_absolute_error: 0.1274 - val_loss: 11.2310 - val_mean_absolute_error: 2.6326 Epoch 956/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0819 - mean_absolute_error: 0.1296 - val_loss: 11.3314 - val_mean_absolute_error: 2.6229 Epoch 957/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0826 - mean_absolute_error: 0.1464 - val_loss: 11.1599 - val_mean_absolute_error: 2.6377 Epoch 958/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1286 - mean_absolute_error: 0.1604 - val_loss: 11.4124 - val_mean_absolute_error: 2.6480 Epoch 959/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0988 - mean_absolute_error: 0.1637 - val_loss: 11.3020 - val_mean_absolute_error: 2.6392 Epoch 960/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1084 - mean_absolute_error: 0.1784 - val_loss: 11.3176 - val_mean_absolute_error: 2.6482 Epoch 961/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0935 - mean_absolute_error: 0.1571 - val_loss: 10.8776 - val_mean_absolute_error: 2.5747 Epoch 962/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0887 - mean_absolute_error: 0.1643 - val_loss: 11.2303 - val_mean_absolute_error: 2.6250 Epoch 963/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1029 - mean_absolute_error: 0.1757 - val_loss: 11.4656 - val_mean_absolute_error: 2.6849 Epoch 964/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1079 - mean_absolute_error: 0.1830 - val_loss: 11.3713 - val_mean_absolute_error: 2.6392 Epoch 965/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1099 - mean_absolute_error: 0.1803 - val_loss: 11.3628 - val_mean_absolute_error: 2.6523 Epoch 966/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0991 - mean_absolute_error: 0.1492 - val_loss: 11.3513 - val_mean_absolute_error: 2.6026 Epoch 967/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1000 - mean_absolute_error: 0.1582 - val_loss: 11.1102 - val_mean_absolute_error: 2.6069 Epoch 968/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0837 - mean_absolute_error: 0.1507 - val_loss: 11.4430 - val_mean_absolute_error: 2.6802 Epoch 969/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0924 - mean_absolute_error: 0.1748 - val_loss: 11.2165 - val_mean_absolute_error: 2.6277 Epoch 970/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0767 - mean_absolute_error: 0.1229 - val_loss: 11.2033 - val_mean_absolute_error: 2.6344 Epoch 971/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0755 - mean_absolute_error: 0.1275 - val_loss: 11.2295 - val_mean_absolute_error: 2.6198 Epoch 972/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0965 - mean_absolute_error: 0.1513 - val_loss: 11.1237 - val_mean_absolute_error: 2.6411 Epoch 973/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0994 - mean_absolute_error: 0.1739 - val_loss: 11.3036 - val_mean_absolute_error: 2.6321 Epoch 974/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0961 - mean_absolute_error: 0.1747 - val_loss: 11.3091 - val_mean_absolute_error: 2.6691 Epoch 975/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1007 - mean_absolute_error: 0.1756 - val_loss: 11.2611 - val_mean_absolute_error: 2.6438 Epoch 976/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0854 - mean_absolute_error: 0.1507 - val_loss: 11.4312 - val_mean_absolute_error: 2.6313 Epoch 977/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0806 - mean_absolute_error: 0.1302 - val_loss: 11.2457 - val_mean_absolute_error: 2.6620 Epoch 978/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0756 - mean_absolute_error: 0.1271 - val_loss: 11.1416 - val_mean_absolute_error: 2.6216 Epoch 979/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0742 - mean_absolute_error: 0.1299 - val_loss: 11.4672 - val_mean_absolute_error: 2.6892 Epoch 980/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0878 - mean_absolute_error: 0.1358 - val_loss: 11.3912 - val_mean_absolute_error: 2.6469 Epoch 981/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.1158 - mean_absolute_error: 0.1777 - val_loss: 11.0629 - val_mean_absolute_error: 2.6128 Epoch 982/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2313 - mean_absolute_error: 0.2780 - val_loss: 11.1152 - val_mean_absolute_error: 2.6529 Epoch 983/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1563 - mean_absolute_error: 0.2394 - val_loss: 11.3794 - val_mean_absolute_error: 2.6305 Epoch 984/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1094 - mean_absolute_error: 0.2046 - val_loss: 11.2741 - val_mean_absolute_error: 2.6715 Epoch 985/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1125 - mean_absolute_error: 0.1864 - val_loss: 11.2821 - val_mean_absolute_error: 2.6111 Epoch 986/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0943 - mean_absolute_error: 0.1693 - val_loss: 10.8967 - val_mean_absolute_error: 2.6015 Epoch 987/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0978 - mean_absolute_error: 0.1691 - val_loss: 11.0920 - val_mean_absolute_error: 2.6258 Epoch 988/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0845 - mean_absolute_error: 0.1451 - val_loss: 11.1667 - val_mean_absolute_error: 2.6404 Epoch 989/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.0961 - mean_absolute_error: 0.1470 - val_loss: 11.4085 - val_mean_absolute_error: 2.6475 Epoch 990/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0919 - mean_absolute_error: 0.1648 - val_loss: 10.9679 - val_mean_absolute_error: 2.6030 Epoch 991/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0794 - mean_absolute_error: 0.1384 - val_loss: 11.2004 - val_mean_absolute_error: 2.6120 Epoch 992/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0708 - mean_absolute_error: 0.1148 - val_loss: 11.1887 - val_mean_absolute_error: 2.6321 Epoch 993/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0765 - mean_absolute_error: 0.1156 - val_loss: 11.0180 - val_mean_absolute_error: 2.6060 Epoch 994/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0694 - mean_absolute_error: 0.0979 - val_loss: 11.0212 - val_mean_absolute_error: 2.6018 Epoch 995/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0758 - mean_absolute_error: 0.1016 - val_loss: 11.1175 - val_mean_absolute_error: 2.6165 Epoch 996/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0781 - mean_absolute_error: 0.1133 - val_loss: 11.2101 - val_mean_absolute_error: 2.6485 Epoch 997/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1017 - mean_absolute_error: 0.1453 - val_loss: 11.2507 - val_mean_absolute_error: 2.6030 Epoch 998/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0993 - mean_absolute_error: 0.1555 - val_loss: 11.2464 - val_mean_absolute_error: 2.6698 Epoch 999/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1071 - mean_absolute_error: 0.1682 - val_loss: 11.3444 - val_mean_absolute_error: 2.6537 Epoch 1000/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1121 - mean_absolute_error: 0.1817 - val_loss: 11.3232 - val_mean_absolute_error: 2.6454 7/7 [==============================] - 1s 5ms/step 1/1 [==============================] - 0s 19ms/step 1/1 [==============================] - 0s 27ms/step
# Calculate the RMSE
rmse_lstm = sqrt(mean_squared_error(y_test_BAC, test_predictions_BAC))
print('The RMSE value of LSTM model (BAC): {:.4f}'.format(rmse_lstm))
The RMSE value of LSTM model (BAC): 1.8953
# Plot Training Observations VS Training Predictions
figure(figsize=(20, 10), dpi=100)
plt.plot(dates_train_BAC, train_predictions_BAC)
plt.plot(dates_train_BAC, y_train_BAC)
plt.title('LSTM: Training Actual Returns/Training Predicted Returns (BAC)', fontsize=16)
plt.legend(['BAC Training Predictions', 'BAC Training Observations'])
# Plot Testing Observations VS Testing Predictions
figure(figsize=(20, 10), dpi=100)
plt.plot(dates_test_BAC, test_predictions_BAC)
plt.plot(dates_test_BAC, y_test_BAC)
plt.title('LSTM: Testing Actual Returns/Testing Predicted Returns (BAC)', fontsize=16)
plt.legend(['BAC Testing Predictions', 'BAC Testing Observations'])
# General Plot (Training, Validation & testing)
figure(figsize=(20, 10), dpi=100)
plt.plot(dates_train_BAC, train_predictions_BAC)
plt.plot(dates_train_BAC, y_train_BAC)
plt.plot(dates_val_BAC, val_predictions_BAC)
plt.plot(dates_val_BAC, y_val_BAC)
plt.plot(dates_test_BAC, test_predictions_BAC)
plt.plot(dates_test_BAC, y_test_BAC)
plt.title('LSTM: General forecasting plot (BAC)', fontsize=16)
plt.legend(['BAC Training Predictions',
'BAC Training Observations',
'BAC Validation Predictions',
'BAC Validation Observations',
'BAC Testing Predictions',
'BAC Testing Observations'])
<matplotlib.legend.Legend at 0x7fa8c1445b80>
# transform a time series dataset into a supervised learning dataset (Input : Output)
def BMW_to_windowed_BMW(BMW_RET, first_date_str, last_date_str, n=3):
first_date = str_to_datetime(first_date_str)
last_date = str_to_datetime(last_date_str)
target_date = first_date
dates_BMW = []
X, Y = [], []
last_time = False
while True:
BMW_subset = BMW_RET.loc[:target_date].tail(n+1)
if len(BMW_subset) != n+1:
print(f'Error: Window of size {n} is too large for date {target_date}')
return
values = BMW_subset['Ret_BMW'].to_numpy()
x, y = values[:-1], values[-1]
dates_BMW.append(target_date)
X.append(x)
Y.append(y)
next_week = BMW_RET.loc[target_date:target_date+datetime.timedelta(days=7)]
next_datetime_str = str(next_week.head(2).tail(1).index.values[0])
next_date_str = next_datetime_str.split('T')[0]
year_month_day = next_date_str.split('-')
year, month, day = year_month_day
next_date = datetime.datetime(day=int(day), month=int(month), year=int(year))
if last_time:
break
target_date = next_date
if target_date == last_date:
last_time = True
ret_BMW = pd.DataFrame({})
ret_BMW['Target Date'] = dates_BMW
X = np.array(X)
for i in range(0, n):
X[:, i]
ret_BMW[f'Target-{n-i}'] = X[:, i]
ret_BMW['Target'] = Y
return ret_BMW
# Start day second time around: '2020-01-03'
windowed_BMW = BMW_to_windowed_BMW(BMW_RET,
'2020-01-03',
'2020-12-30',
n=3)
# Convert our new dataset into numpy arrays (to feed it directly into a tensorflow model)
def windowed_BMW_to_date_X_y(windowed_dataframe):
BMW_as_np = windowed_dataframe.to_numpy()
dates_BMW = BMW_as_np[:, 0]
middle_matrix_BMW = BMW_as_np[:, 1:-1]
X_BMW = middle_matrix_BMW.reshape((len(dates_BMW), middle_matrix_BMW.shape[1], 1))
Y_BMW = BMW_as_np[:, -1]
return dates_BMW, X_BMW.astype(np.float32), Y_BMW.astype(np.float32)
dates_BMW, X_BMW, y_BMW = windowed_BMW_to_date_X_y(windowed_BMW)
# Split the data into training, validation and testing partitions
q_85_BMW = int(len(dates_BMW) * .85)
q_95_BMW = int(len(dates_BMW) * .95)
dates_train_BMW, X_train_BMW, y_train_BMW = dates_BMW[:q_85_BMW], X_BMW[:q_85_BMW], y_BMW[:q_85_BMW]
dates_val_BMW, X_val_BMW, y_val_BMW = dates_BMW[q_85_BMW:q_95_BMW], X_BMW[q_85_BMW:q_95_BMW], y_BMW[q_85_BMW:q_95_BMW]
dates_test_BMW, X_test_BMW, y_test_BMW = dates_BMW[q_95_BMW:], X_BMW[q_95_BMW:], y_BMW[q_95_BMW:]
# Create & train the LSTM model
model_BMW = Sequential([layers.Input((3, 1)),
layers.LSTM(264),
layers.Dense(132, activation='relu'),
layers.Dense(132, activation='relu'),
layers.Dense(1)])
model_BMW.compile(loss='mse',
optimizer=Adam(learning_rate=0.001),
metrics=['mean_absolute_error'])
# Fitting the LSTM model
model_BMW.fit(X_train_BMW, y_train_BMW, validation_data=(X_val_BMW, y_val_BMW), epochs=1000)
# Forecasting
train_predictions_BMW = model_BMW.predict(X_train_BMW).flatten()
val_predictions_BMW = model_BMW.predict(X_val_BMW).flatten()
test_predictions_BMW = model_BMW.predict(X_test_BMW).flatten()
Epoch 1/1000 7/7 [==============================] - 3s 110ms/step - loss: 9.2244 - mean_absolute_error: 2.0078 - val_loss: 4.1963 - val_mean_absolute_error: 1.4144 Epoch 2/1000 7/7 [==============================] - 0s 30ms/step - loss: 9.1848 - mean_absolute_error: 2.0069 - val_loss: 4.4034 - val_mean_absolute_error: 1.4704 Epoch 3/1000 7/7 [==============================] - 0s 25ms/step - loss: 9.1018 - mean_absolute_error: 1.9936 - val_loss: 4.4094 - val_mean_absolute_error: 1.4801 Epoch 4/1000 7/7 [==============================] - 0s 25ms/step - loss: 9.0437 - mean_absolute_error: 1.9896 - val_loss: 4.3704 - val_mean_absolute_error: 1.4788 Epoch 5/1000 7/7 [==============================] - 0s 22ms/step - loss: 8.9346 - mean_absolute_error: 1.9820 - val_loss: 4.3977 - val_mean_absolute_error: 1.4899 Epoch 6/1000 7/7 [==============================] - 0s 23ms/step - loss: 8.8733 - mean_absolute_error: 1.9855 - val_loss: 4.5701 - val_mean_absolute_error: 1.5347 Epoch 7/1000 7/7 [==============================] - 0s 26ms/step - loss: 8.8471 - mean_absolute_error: 1.9936 - val_loss: 4.7628 - val_mean_absolute_error: 1.5747 Epoch 8/1000 7/7 [==============================] - 0s 27ms/step - loss: 8.6824 - mean_absolute_error: 1.9830 - val_loss: 4.8329 - val_mean_absolute_error: 1.5916 Epoch 9/1000 7/7 [==============================] - 0s 26ms/step - loss: 8.5197 - mean_absolute_error: 1.9621 - val_loss: 4.8599 - val_mean_absolute_error: 1.6162 Epoch 10/1000 7/7 [==============================] - 0s 21ms/step - loss: 8.3572 - mean_absolute_error: 1.9550 - val_loss: 4.4562 - val_mean_absolute_error: 1.5505 Epoch 11/1000 7/7 [==============================] - 0s 25ms/step - loss: 8.1227 - mean_absolute_error: 1.9331 - val_loss: 5.5949 - val_mean_absolute_error: 1.7538 Epoch 12/1000 7/7 [==============================] - 0s 24ms/step - loss: 7.8975 - mean_absolute_error: 1.9203 - val_loss: 5.2890 - val_mean_absolute_error: 1.7185 Epoch 13/1000 7/7 [==============================] - 0s 22ms/step - loss: 7.5778 - mean_absolute_error: 1.9250 - val_loss: 4.8628 - val_mean_absolute_error: 1.6621 Epoch 14/1000 7/7 [==============================] - 0s 24ms/step - loss: 7.1852 - mean_absolute_error: 1.8807 - val_loss: 4.4441 - val_mean_absolute_error: 1.6009 Epoch 15/1000 7/7 [==============================] - 0s 29ms/step - loss: 7.0847 - mean_absolute_error: 1.8664 - val_loss: 5.8303 - val_mean_absolute_error: 1.8077 Epoch 16/1000 7/7 [==============================] - 0s 26ms/step - loss: 6.9079 - mean_absolute_error: 1.8417 - val_loss: 5.8020 - val_mean_absolute_error: 1.7910 Epoch 17/1000 7/7 [==============================] - 0s 27ms/step - loss: 6.7662 - mean_absolute_error: 1.7933 - val_loss: 4.9335 - val_mean_absolute_error: 1.6636 Epoch 18/1000 7/7 [==============================] - 0s 27ms/step - loss: 6.4134 - mean_absolute_error: 1.8019 - val_loss: 4.9147 - val_mean_absolute_error: 1.6698 Epoch 19/1000 7/7 [==============================] - 0s 26ms/step - loss: 6.2398 - mean_absolute_error: 1.8193 - val_loss: 5.5441 - val_mean_absolute_error: 1.7807 Epoch 20/1000 7/7 [==============================] - 0s 32ms/step - loss: 6.0101 - mean_absolute_error: 1.7651 - val_loss: 6.2547 - val_mean_absolute_error: 1.8864 Epoch 21/1000 7/7 [==============================] - 0s 21ms/step - loss: 6.0010 - mean_absolute_error: 1.7512 - val_loss: 5.0777 - val_mean_absolute_error: 1.6812 Epoch 22/1000 7/7 [==============================] - 0s 23ms/step - loss: 5.8023 - mean_absolute_error: 1.7375 - val_loss: 4.8112 - val_mean_absolute_error: 1.6322 Epoch 23/1000 7/7 [==============================] - 0s 22ms/step - loss: 5.8751 - mean_absolute_error: 1.7298 - val_loss: 4.4381 - val_mean_absolute_error: 1.5401 Epoch 24/1000 7/7 [==============================] - 0s 32ms/step - loss: 8.0805 - mean_absolute_error: 1.9160 - val_loss: 4.8904 - val_mean_absolute_error: 1.6353 Epoch 25/1000 7/7 [==============================] - 0s 27ms/step - loss: 6.0141 - mean_absolute_error: 1.7438 - val_loss: 5.4825 - val_mean_absolute_error: 1.7460 Epoch 26/1000 7/7 [==============================] - 0s 24ms/step - loss: 6.0345 - mean_absolute_error: 1.7679 - val_loss: 4.6707 - val_mean_absolute_error: 1.6196 Epoch 27/1000 7/7 [==============================] - 0s 28ms/step - loss: 5.8384 - mean_absolute_error: 1.7263 - val_loss: 4.1634 - val_mean_absolute_error: 1.5174 Epoch 28/1000 7/7 [==============================] - 0s 27ms/step - loss: 5.6189 - mean_absolute_error: 1.6890 - val_loss: 6.0335 - val_mean_absolute_error: 1.8429 Epoch 29/1000 7/7 [==============================] - 0s 24ms/step - loss: 5.6530 - mean_absolute_error: 1.7148 - val_loss: 5.2906 - val_mean_absolute_error: 1.7188 Epoch 30/1000 7/7 [==============================] - 0s 22ms/step - loss: 5.6147 - mean_absolute_error: 1.6890 - val_loss: 4.1774 - val_mean_absolute_error: 1.5439 Epoch 31/1000 7/7 [==============================] - 0s 27ms/step - loss: 5.4768 - mean_absolute_error: 1.6639 - val_loss: 5.4316 - val_mean_absolute_error: 1.7537 Epoch 32/1000 7/7 [==============================] - 0s 25ms/step - loss: 5.2931 - mean_absolute_error: 1.6515 - val_loss: 5.2774 - val_mean_absolute_error: 1.7227 Epoch 33/1000 7/7 [==============================] - 0s 26ms/step - loss: 5.1996 - mean_absolute_error: 1.6262 - val_loss: 4.9823 - val_mean_absolute_error: 1.6795 Epoch 34/1000 7/7 [==============================] - 0s 25ms/step - loss: 5.2868 - mean_absolute_error: 1.6447 - val_loss: 5.0298 - val_mean_absolute_error: 1.6891 Epoch 35/1000 7/7 [==============================] - 0s 28ms/step - loss: 5.0828 - mean_absolute_error: 1.6188 - val_loss: 5.9036 - val_mean_absolute_error: 1.8282 Epoch 36/1000 7/7 [==============================] - 0s 25ms/step - loss: 5.0931 - mean_absolute_error: 1.6189 - val_loss: 4.9521 - val_mean_absolute_error: 1.6838 Epoch 37/1000 7/7 [==============================] - 0s 20ms/step - loss: 4.9963 - mean_absolute_error: 1.5782 - val_loss: 5.2415 - val_mean_absolute_error: 1.7157 Epoch 38/1000 7/7 [==============================] - 0s 19ms/step - loss: 5.0815 - mean_absolute_error: 1.6153 - val_loss: 6.5121 - val_mean_absolute_error: 1.9137 Epoch 39/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.8654 - mean_absolute_error: 1.5745 - val_loss: 4.1035 - val_mean_absolute_error: 1.5189 Epoch 40/1000 7/7 [==============================] - 0s 19ms/step - loss: 5.0575 - mean_absolute_error: 1.5894 - val_loss: 4.5927 - val_mean_absolute_error: 1.5969 Epoch 41/1000 7/7 [==============================] - 0s 26ms/step - loss: 4.8597 - mean_absolute_error: 1.5776 - val_loss: 5.9205 - val_mean_absolute_error: 1.8128 Epoch 42/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.9186 - mean_absolute_error: 1.5913 - val_loss: 6.0286 - val_mean_absolute_error: 1.8370 Epoch 43/1000 7/7 [==============================] - 0s 20ms/step - loss: 4.9361 - mean_absolute_error: 1.5694 - val_loss: 4.7951 - val_mean_absolute_error: 1.6370 Epoch 44/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.7974 - mean_absolute_error: 1.5616 - val_loss: 5.8447 - val_mean_absolute_error: 1.8385 Epoch 45/1000 7/7 [==============================] - 0s 26ms/step - loss: 4.8634 - mean_absolute_error: 1.5593 - val_loss: 5.8015 - val_mean_absolute_error: 1.8108 Epoch 46/1000 7/7 [==============================] - 0s 21ms/step - loss: 4.7249 - mean_absolute_error: 1.5864 - val_loss: 6.8052 - val_mean_absolute_error: 1.9833 Epoch 47/1000 7/7 [==============================] - 0s 21ms/step - loss: 4.7156 - mean_absolute_error: 1.5631 - val_loss: 5.1090 - val_mean_absolute_error: 1.6742 Epoch 48/1000 7/7 [==============================] - 0s 23ms/step - loss: 4.6874 - mean_absolute_error: 1.5586 - val_loss: 5.8968 - val_mean_absolute_error: 1.8164 Epoch 49/1000 7/7 [==============================] - 0s 22ms/step - loss: 4.5835 - mean_absolute_error: 1.5399 - val_loss: 4.6425 - val_mean_absolute_error: 1.6284 Epoch 50/1000 7/7 [==============================] - 0s 20ms/step - loss: 4.8136 - mean_absolute_error: 1.5827 - val_loss: 5.1649 - val_mean_absolute_error: 1.6951 Epoch 51/1000 7/7 [==============================] - 0s 20ms/step - loss: 4.6280 - mean_absolute_error: 1.5712 - val_loss: 6.0887 - val_mean_absolute_error: 1.8506 Epoch 52/1000 7/7 [==============================] - 0s 20ms/step - loss: 4.6440 - mean_absolute_error: 1.5770 - val_loss: 5.4791 - val_mean_absolute_error: 1.7504 Epoch 53/1000 7/7 [==============================] - 0s 24ms/step - loss: 5.3037 - mean_absolute_error: 1.6391 - val_loss: 5.5614 - val_mean_absolute_error: 1.7565 Epoch 54/1000 7/7 [==============================] - 0s 20ms/step - loss: 4.9740 - mean_absolute_error: 1.6481 - val_loss: 6.0916 - val_mean_absolute_error: 1.8357 Epoch 55/1000 7/7 [==============================] - 0s 20ms/step - loss: 4.8725 - mean_absolute_error: 1.6068 - val_loss: 5.1977 - val_mean_absolute_error: 1.7098 Epoch 56/1000 7/7 [==============================] - 0s 19ms/step - loss: 5.0443 - mean_absolute_error: 1.6062 - val_loss: 5.6040 - val_mean_absolute_error: 1.7448 Epoch 57/1000 7/7 [==============================] - 0s 20ms/step - loss: 4.7368 - mean_absolute_error: 1.6016 - val_loss: 5.9243 - val_mean_absolute_error: 1.7774 Epoch 58/1000 7/7 [==============================] - 0s 18ms/step - loss: 4.4323 - mean_absolute_error: 1.5220 - val_loss: 5.3792 - val_mean_absolute_error: 1.7120 Epoch 59/1000 7/7 [==============================] - 0s 18ms/step - loss: 4.4676 - mean_absolute_error: 1.5208 - val_loss: 6.0015 - val_mean_absolute_error: 1.8181 Epoch 60/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.4306 - mean_absolute_error: 1.5113 - val_loss: 6.4825 - val_mean_absolute_error: 1.8735 Epoch 61/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.3093 - mean_absolute_error: 1.4895 - val_loss: 5.2506 - val_mean_absolute_error: 1.7042 Epoch 62/1000 7/7 [==============================] - 0s 18ms/step - loss: 4.2495 - mean_absolute_error: 1.4764 - val_loss: 5.8570 - val_mean_absolute_error: 1.7972 Epoch 63/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.2338 - mean_absolute_error: 1.4802 - val_loss: 5.8019 - val_mean_absolute_error: 1.7788 Epoch 64/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.1490 - mean_absolute_error: 1.4631 - val_loss: 5.9618 - val_mean_absolute_error: 1.7934 Epoch 65/1000 7/7 [==============================] - 0s 30ms/step - loss: 4.1620 - mean_absolute_error: 1.4582 - val_loss: 6.4314 - val_mean_absolute_error: 1.8871 Epoch 66/1000 7/7 [==============================] - 0s 20ms/step - loss: 4.2845 - mean_absolute_error: 1.4913 - val_loss: 5.3353 - val_mean_absolute_error: 1.6316 Epoch 67/1000 7/7 [==============================] - 0s 17ms/step - loss: 4.1063 - mean_absolute_error: 1.4659 - val_loss: 6.9942 - val_mean_absolute_error: 1.9452 Epoch 68/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.1593 - mean_absolute_error: 1.4784 - val_loss: 6.6622 - val_mean_absolute_error: 1.9369 Epoch 69/1000 7/7 [==============================] - 0s 18ms/step - loss: 4.2263 - mean_absolute_error: 1.4912 - val_loss: 5.0431 - val_mean_absolute_error: 1.5899 Epoch 70/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.0303 - mean_absolute_error: 1.4863 - val_loss: 7.0934 - val_mean_absolute_error: 1.9309 Epoch 71/1000 7/7 [==============================] - 0s 18ms/step - loss: 3.9364 - mean_absolute_error: 1.4319 - val_loss: 5.6681 - val_mean_absolute_error: 1.7565 Epoch 72/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.9806 - mean_absolute_error: 1.4477 - val_loss: 6.6652 - val_mean_absolute_error: 1.8979 Epoch 73/1000 7/7 [==============================] - 0s 23ms/step - loss: 4.0739 - mean_absolute_error: 1.4778 - val_loss: 6.6826 - val_mean_absolute_error: 1.8833 Epoch 74/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.8507 - mean_absolute_error: 1.4290 - val_loss: 5.6335 - val_mean_absolute_error: 1.7471 Epoch 75/1000 7/7 [==============================] - 0s 18ms/step - loss: 3.9241 - mean_absolute_error: 1.4522 - val_loss: 7.3716 - val_mean_absolute_error: 1.9865 Epoch 76/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.1223 - mean_absolute_error: 1.4920 - val_loss: 6.4916 - val_mean_absolute_error: 1.8313 Epoch 77/1000 7/7 [==============================] - 0s 21ms/step - loss: 4.3310 - mean_absolute_error: 1.5176 - val_loss: 4.6770 - val_mean_absolute_error: 1.5725 Epoch 78/1000 7/7 [==============================] - 0s 18ms/step - loss: 4.1479 - mean_absolute_error: 1.4904 - val_loss: 8.4678 - val_mean_absolute_error: 2.1453 Epoch 79/1000 7/7 [==============================] - 0s 18ms/step - loss: 4.0095 - mean_absolute_error: 1.4836 - val_loss: 4.8887 - val_mean_absolute_error: 1.6679 Epoch 80/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.0462 - mean_absolute_error: 1.4564 - val_loss: 5.7196 - val_mean_absolute_error: 1.7961 Epoch 81/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.9921 - mean_absolute_error: 1.4673 - val_loss: 6.8410 - val_mean_absolute_error: 1.9473 Epoch 82/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.8732 - mean_absolute_error: 1.4533 - val_loss: 5.4718 - val_mean_absolute_error: 1.7294 Epoch 83/1000 7/7 [==============================] - 0s 20ms/step - loss: 4.1376 - mean_absolute_error: 1.4636 - val_loss: 6.6659 - val_mean_absolute_error: 1.8857 Epoch 84/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.6940 - mean_absolute_error: 1.4020 - val_loss: 6.0998 - val_mean_absolute_error: 1.7976 Epoch 85/1000 7/7 [==============================] - 0s 18ms/step - loss: 3.9258 - mean_absolute_error: 1.4386 - val_loss: 7.0033 - val_mean_absolute_error: 1.9748 Epoch 86/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.4058 - mean_absolute_error: 1.5428 - val_loss: 5.6848 - val_mean_absolute_error: 1.7032 Epoch 87/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.8271 - mean_absolute_error: 1.4313 - val_loss: 6.9041 - val_mean_absolute_error: 1.9195 Epoch 88/1000 7/7 [==============================] - 0s 28ms/step - loss: 4.0342 - mean_absolute_error: 1.4582 - val_loss: 6.9867 - val_mean_absolute_error: 1.9118 Epoch 89/1000 7/7 [==============================] - 0s 20ms/step - loss: 3.6355 - mean_absolute_error: 1.3923 - val_loss: 5.8786 - val_mean_absolute_error: 1.7217 Epoch 90/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.7730 - mean_absolute_error: 1.4221 - val_loss: 7.4170 - val_mean_absolute_error: 2.0006 Epoch 91/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.7654 - mean_absolute_error: 1.4263 - val_loss: 7.1070 - val_mean_absolute_error: 1.9660 Epoch 92/1000 7/7 [==============================] - 0s 18ms/step - loss: 3.7027 - mean_absolute_error: 1.3999 - val_loss: 6.4420 - val_mean_absolute_error: 1.8461 Epoch 93/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.3975 - mean_absolute_error: 1.3589 - val_loss: 7.3641 - val_mean_absolute_error: 1.9317 Epoch 94/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.4035 - mean_absolute_error: 1.3558 - val_loss: 6.5203 - val_mean_absolute_error: 1.8514 Epoch 95/1000 7/7 [==============================] - 0s 18ms/step - loss: 3.2940 - mean_absolute_error: 1.3126 - val_loss: 6.9403 - val_mean_absolute_error: 1.9203 Epoch 96/1000 7/7 [==============================] - 0s 18ms/step - loss: 3.1839 - mean_absolute_error: 1.2898 - val_loss: 7.3520 - val_mean_absolute_error: 1.9076 Epoch 97/1000 7/7 [==============================] - 0s 18ms/step - loss: 3.3088 - mean_absolute_error: 1.3393 - val_loss: 7.3261 - val_mean_absolute_error: 1.8839 Epoch 98/1000 7/7 [==============================] - 0s 18ms/step - loss: 3.4045 - mean_absolute_error: 1.3496 - val_loss: 6.7062 - val_mean_absolute_error: 1.8985 Epoch 99/1000 7/7 [==============================] - 0s 20ms/step - loss: 3.0813 - mean_absolute_error: 1.2974 - val_loss: 8.5337 - val_mean_absolute_error: 2.0491 Epoch 100/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.0747 - mean_absolute_error: 1.2792 - val_loss: 6.1616 - val_mean_absolute_error: 1.7792 Epoch 101/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.2975 - mean_absolute_error: 1.3256 - val_loss: 8.6968 - val_mean_absolute_error: 2.1249 Epoch 102/1000 7/7 [==============================] - 0s 20ms/step - loss: 3.1137 - mean_absolute_error: 1.2999 - val_loss: 7.4863 - val_mean_absolute_error: 1.9088 Epoch 103/1000 7/7 [==============================] - 0s 18ms/step - loss: 2.9998 - mean_absolute_error: 1.2540 - val_loss: 7.2050 - val_mean_absolute_error: 1.9846 Epoch 104/1000 7/7 [==============================] - 0s 18ms/step - loss: 2.8672 - mean_absolute_error: 1.2218 - val_loss: 8.0776 - val_mean_absolute_error: 2.0574 Epoch 105/1000 7/7 [==============================] - 0s 18ms/step - loss: 2.9359 - mean_absolute_error: 1.2769 - val_loss: 6.2774 - val_mean_absolute_error: 1.8254 Epoch 106/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.8213 - mean_absolute_error: 1.2409 - val_loss: 7.9148 - val_mean_absolute_error: 2.0059 Epoch 107/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.8023 - mean_absolute_error: 1.2400 - val_loss: 7.0492 - val_mean_absolute_error: 1.9049 Epoch 108/1000 7/7 [==============================] - 0s 18ms/step - loss: 2.7374 - mean_absolute_error: 1.2023 - val_loss: 8.2303 - val_mean_absolute_error: 2.0900 Epoch 109/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.8059 - mean_absolute_error: 1.2422 - val_loss: 7.7433 - val_mean_absolute_error: 2.0203 Epoch 110/1000 7/7 [==============================] - 0s 18ms/step - loss: 2.7419 - mean_absolute_error: 1.2164 - val_loss: 7.6234 - val_mean_absolute_error: 2.0240 Epoch 111/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.7110 - mean_absolute_error: 1.1634 - val_loss: 7.7090 - val_mean_absolute_error: 2.0059 Epoch 112/1000 7/7 [==============================] - 0s 18ms/step - loss: 2.7313 - mean_absolute_error: 1.2108 - val_loss: 8.1664 - val_mean_absolute_error: 2.0268 Epoch 113/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.5663 - mean_absolute_error: 1.1485 - val_loss: 7.9036 - val_mean_absolute_error: 2.0170 Epoch 114/1000 7/7 [==============================] - 0s 22ms/step - loss: 2.4266 - mean_absolute_error: 1.0937 - val_loss: 8.4404 - val_mean_absolute_error: 2.0767 Epoch 115/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.4255 - mean_absolute_error: 1.0930 - val_loss: 8.1952 - val_mean_absolute_error: 2.0785 Epoch 116/1000 7/7 [==============================] - 0s 36ms/step - loss: 2.4156 - mean_absolute_error: 1.1228 - val_loss: 7.8497 - val_mean_absolute_error: 2.0578 Epoch 117/1000 7/7 [==============================] - 0s 24ms/step - loss: 2.4883 - mean_absolute_error: 1.1447 - val_loss: 8.9549 - val_mean_absolute_error: 2.1396 Epoch 118/1000 7/7 [==============================] - 0s 25ms/step - loss: 2.4818 - mean_absolute_error: 1.1221 - val_loss: 7.0417 - val_mean_absolute_error: 1.9571 Epoch 119/1000 7/7 [==============================] - 0s 26ms/step - loss: 2.4924 - mean_absolute_error: 1.1484 - val_loss: 10.2599 - val_mean_absolute_error: 2.2731 Epoch 120/1000 7/7 [==============================] - 0s 21ms/step - loss: 2.5134 - mean_absolute_error: 1.1397 - val_loss: 7.7537 - val_mean_absolute_error: 2.0460 Epoch 121/1000 7/7 [==============================] - 0s 20ms/step - loss: 2.3422 - mean_absolute_error: 1.1298 - val_loss: 8.2953 - val_mean_absolute_error: 2.0751 Epoch 122/1000 7/7 [==============================] - 0s 21ms/step - loss: 2.1493 - mean_absolute_error: 1.0373 - val_loss: 9.0435 - val_mean_absolute_error: 2.1447 Epoch 123/1000 7/7 [==============================] - 0s 21ms/step - loss: 2.2709 - mean_absolute_error: 1.0660 - val_loss: 7.9002 - val_mean_absolute_error: 2.0890 Epoch 124/1000 7/7 [==============================] - 0s 20ms/step - loss: 2.3297 - mean_absolute_error: 1.1226 - val_loss: 8.4695 - val_mean_absolute_error: 2.0866 Epoch 125/1000 7/7 [==============================] - 0s 20ms/step - loss: 2.1664 - mean_absolute_error: 1.0307 - val_loss: 9.0073 - val_mean_absolute_error: 2.1310 Epoch 126/1000 7/7 [==============================] - 0s 21ms/step - loss: 2.3115 - mean_absolute_error: 1.0961 - val_loss: 10.2172 - val_mean_absolute_error: 2.2567 Epoch 127/1000 7/7 [==============================] - 0s 21ms/step - loss: 2.1088 - mean_absolute_error: 1.0437 - val_loss: 8.0322 - val_mean_absolute_error: 2.1127 Epoch 128/1000 7/7 [==============================] - 0s 21ms/step - loss: 2.0799 - mean_absolute_error: 1.0290 - val_loss: 9.6314 - val_mean_absolute_error: 2.2091 Epoch 129/1000 7/7 [==============================] - 0s 26ms/step - loss: 1.9660 - mean_absolute_error: 1.0084 - val_loss: 8.1147 - val_mean_absolute_error: 2.1954 Epoch 130/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.1008 - mean_absolute_error: 1.0334 - val_loss: 11.0152 - val_mean_absolute_error: 2.3543 Epoch 131/1000 7/7 [==============================] - 0s 17ms/step - loss: 2.1549 - mean_absolute_error: 1.0712 - val_loss: 8.0927 - val_mean_absolute_error: 2.1655 Epoch 132/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.1254 - mean_absolute_error: 1.0532 - val_loss: 9.8075 - val_mean_absolute_error: 2.2189 Epoch 133/1000 7/7 [==============================] - 0s 17ms/step - loss: 1.9383 - mean_absolute_error: 1.0027 - val_loss: 10.0022 - val_mean_absolute_error: 2.2469 Epoch 134/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.9506 - mean_absolute_error: 1.0194 - val_loss: 8.8419 - val_mean_absolute_error: 2.1789 Epoch 135/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.9032 - mean_absolute_error: 0.9820 - val_loss: 10.4006 - val_mean_absolute_error: 2.2910 Epoch 136/1000 7/7 [==============================] - 0s 17ms/step - loss: 1.8856 - mean_absolute_error: 0.9742 - val_loss: 9.5521 - val_mean_absolute_error: 2.2535 Epoch 137/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.0534 - mean_absolute_error: 1.0133 - val_loss: 8.2896 - val_mean_absolute_error: 2.1740 Epoch 138/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.1622 - mean_absolute_error: 1.0663 - val_loss: 10.9254 - val_mean_absolute_error: 2.3217 Epoch 139/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.2665 - mean_absolute_error: 1.1283 - val_loss: 8.2426 - val_mean_absolute_error: 2.2457 Epoch 140/1000 7/7 [==============================] - 0s 18ms/step - loss: 2.3328 - mean_absolute_error: 1.1204 - val_loss: 11.3268 - val_mean_absolute_error: 2.4048 Epoch 141/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.2784 - mean_absolute_error: 1.1362 - val_loss: 8.1857 - val_mean_absolute_error: 2.2289 Epoch 142/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.3057 - mean_absolute_error: 1.1317 - val_loss: 10.2976 - val_mean_absolute_error: 2.3172 Epoch 143/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.9487 - mean_absolute_error: 1.0147 - val_loss: 8.7907 - val_mean_absolute_error: 2.1852 Epoch 144/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.9244 - mean_absolute_error: 0.9901 - val_loss: 10.1586 - val_mean_absolute_error: 2.3065 Epoch 145/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.7946 - mean_absolute_error: 0.9608 - val_loss: 9.5445 - val_mean_absolute_error: 2.2234 Epoch 146/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.8934 - mean_absolute_error: 1.0105 - val_loss: 10.0270 - val_mean_absolute_error: 2.2697 Epoch 147/1000 7/7 [==============================] - 0s 20ms/step - loss: 2.2636 - mean_absolute_error: 1.0878 - val_loss: 8.6834 - val_mean_absolute_error: 2.1706 Epoch 148/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.1943 - mean_absolute_error: 1.0796 - val_loss: 11.5819 - val_mean_absolute_error: 2.3760 Epoch 149/1000 7/7 [==============================] - 0s 18ms/step - loss: 2.0576 - mean_absolute_error: 1.0972 - val_loss: 7.8778 - val_mean_absolute_error: 2.2666 Epoch 150/1000 7/7 [==============================] - 0s 20ms/step - loss: 2.1612 - mean_absolute_error: 1.1246 - val_loss: 11.3373 - val_mean_absolute_error: 2.2993 Epoch 151/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.8595 - mean_absolute_error: 1.0262 - val_loss: 9.1254 - val_mean_absolute_error: 2.2548 Epoch 152/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.8011 - mean_absolute_error: 0.9996 - val_loss: 9.4454 - val_mean_absolute_error: 2.2379 Epoch 153/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.6430 - mean_absolute_error: 0.9240 - val_loss: 10.0740 - val_mean_absolute_error: 2.2188 Epoch 154/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.7741 - mean_absolute_error: 0.9836 - val_loss: 9.1974 - val_mean_absolute_error: 2.2419 Epoch 155/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.5947 - mean_absolute_error: 0.9443 - val_loss: 9.3041 - val_mean_absolute_error: 2.2396 Epoch 156/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.7812 - mean_absolute_error: 0.9473 - val_loss: 11.1087 - val_mean_absolute_error: 2.3344 Epoch 157/1000 7/7 [==============================] - 0s 17ms/step - loss: 2.0260 - mean_absolute_error: 1.0999 - val_loss: 8.9386 - val_mean_absolute_error: 2.2971 Epoch 158/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.6627 - mean_absolute_error: 0.9527 - val_loss: 12.0299 - val_mean_absolute_error: 2.4024 Epoch 159/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.6967 - mean_absolute_error: 0.9706 - val_loss: 8.8274 - val_mean_absolute_error: 2.3413 Epoch 160/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.8405 - mean_absolute_error: 1.0013 - val_loss: 14.3685 - val_mean_absolute_error: 2.7107 Epoch 161/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.7643 - mean_absolute_error: 1.0126 - val_loss: 8.5345 - val_mean_absolute_error: 2.2928 Epoch 162/1000 7/7 [==============================] - 0s 30ms/step - loss: 1.6129 - mean_absolute_error: 0.9463 - val_loss: 12.4551 - val_mean_absolute_error: 2.4448 Epoch 163/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.5040 - mean_absolute_error: 0.8908 - val_loss: 9.2868 - val_mean_absolute_error: 2.3023 Epoch 164/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.3789 - mean_absolute_error: 0.8696 - val_loss: 10.9330 - val_mean_absolute_error: 2.3142 Epoch 165/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.3122 - mean_absolute_error: 0.8140 - val_loss: 9.4694 - val_mean_absolute_error: 2.2638 Epoch 166/1000 7/7 [==============================] - 0s 33ms/step - loss: 1.3191 - mean_absolute_error: 0.8134 - val_loss: 11.0353 - val_mean_absolute_error: 2.3557 Epoch 167/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.3833 - mean_absolute_error: 0.8568 - val_loss: 8.5396 - val_mean_absolute_error: 2.2471 Epoch 168/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.3247 - mean_absolute_error: 0.8279 - val_loss: 12.7207 - val_mean_absolute_error: 2.5166 Epoch 169/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.4979 - mean_absolute_error: 0.8651 - val_loss: 9.2735 - val_mean_absolute_error: 2.3103 Epoch 170/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.5321 - mean_absolute_error: 0.8986 - val_loss: 12.3985 - val_mean_absolute_error: 2.5691 Epoch 171/1000 7/7 [==============================] - 0s 22ms/step - loss: 1.3644 - mean_absolute_error: 0.8693 - val_loss: 9.4814 - val_mean_absolute_error: 2.2611 Epoch 172/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.2811 - mean_absolute_error: 0.8008 - val_loss: 11.2137 - val_mean_absolute_error: 2.3523 Epoch 173/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.2419 - mean_absolute_error: 0.7653 - val_loss: 9.7253 - val_mean_absolute_error: 2.2936 Epoch 174/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.3083 - mean_absolute_error: 0.8168 - val_loss: 10.6049 - val_mean_absolute_error: 2.3275 Epoch 175/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.3498 - mean_absolute_error: 0.8452 - val_loss: 9.6151 - val_mean_absolute_error: 2.2892 Epoch 176/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.3026 - mean_absolute_error: 0.8355 - val_loss: 11.4195 - val_mean_absolute_error: 2.3307 Epoch 177/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.4725 - mean_absolute_error: 0.8757 - val_loss: 9.2746 - val_mean_absolute_error: 2.3180 Epoch 178/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.4252 - mean_absolute_error: 0.9028 - val_loss: 13.4209 - val_mean_absolute_error: 2.5807 Epoch 179/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.3213 - mean_absolute_error: 0.8699 - val_loss: 9.4083 - val_mean_absolute_error: 2.3670 Epoch 180/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.2920 - mean_absolute_error: 0.8422 - val_loss: 12.3839 - val_mean_absolute_error: 2.4383 Epoch 181/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.3021 - mean_absolute_error: 0.8464 - val_loss: 9.3092 - val_mean_absolute_error: 2.3388 Epoch 182/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.2418 - mean_absolute_error: 0.8058 - val_loss: 11.3785 - val_mean_absolute_error: 2.3789 Epoch 183/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.3433 - mean_absolute_error: 0.8376 - val_loss: 10.1410 - val_mean_absolute_error: 2.3802 Epoch 184/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.0902 - mean_absolute_error: 0.7399 - val_loss: 10.8392 - val_mean_absolute_error: 2.3478 Epoch 185/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.1362 - mean_absolute_error: 0.7299 - val_loss: 10.1163 - val_mean_absolute_error: 2.3214 Epoch 186/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.1429 - mean_absolute_error: 0.7440 - val_loss: 11.1999 - val_mean_absolute_error: 2.3442 Epoch 187/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.0219 - mean_absolute_error: 0.7125 - val_loss: 11.1222 - val_mean_absolute_error: 2.3510 Epoch 188/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.0204 - mean_absolute_error: 0.7054 - val_loss: 10.6766 - val_mean_absolute_error: 2.3815 Epoch 189/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.0826 - mean_absolute_error: 0.7423 - val_loss: 11.4094 - val_mean_absolute_error: 2.3501 Epoch 190/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.9941 - mean_absolute_error: 0.6938 - val_loss: 9.8968 - val_mean_absolute_error: 2.3329 Epoch 191/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.0106 - mean_absolute_error: 0.6997 - val_loss: 12.0554 - val_mean_absolute_error: 2.4215 Epoch 192/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.9715 - mean_absolute_error: 0.6789 - val_loss: 10.2187 - val_mean_absolute_error: 2.3283 Epoch 193/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.9323 - mean_absolute_error: 0.6699 - val_loss: 10.6990 - val_mean_absolute_error: 2.3459 Epoch 194/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.9137 - mean_absolute_error: 0.6724 - val_loss: 10.9569 - val_mean_absolute_error: 2.3692 Epoch 195/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.9105 - mean_absolute_error: 0.6488 - val_loss: 10.9843 - val_mean_absolute_error: 2.3213 Epoch 196/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.9223 - mean_absolute_error: 0.6754 - val_loss: 10.4069 - val_mean_absolute_error: 2.3061 Epoch 197/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.9931 - mean_absolute_error: 0.6987 - val_loss: 12.0836 - val_mean_absolute_error: 2.4336 Epoch 198/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.0011 - mean_absolute_error: 0.7036 - val_loss: 9.8302 - val_mean_absolute_error: 2.3160 Epoch 199/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.9293 - mean_absolute_error: 0.6739 - val_loss: 12.9734 - val_mean_absolute_error: 2.4859 Epoch 200/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.9500 - mean_absolute_error: 0.6830 - val_loss: 10.0728 - val_mean_absolute_error: 2.3548 Epoch 201/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.8323 - mean_absolute_error: 0.6303 - val_loss: 11.6384 - val_mean_absolute_error: 2.3820 Epoch 202/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.8124 - mean_absolute_error: 0.6098 - val_loss: 9.8363 - val_mean_absolute_error: 2.3292 Epoch 203/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.8409 - mean_absolute_error: 0.6353 - val_loss: 11.9078 - val_mean_absolute_error: 2.4207 Epoch 204/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.8685 - mean_absolute_error: 0.6559 - val_loss: 11.0062 - val_mean_absolute_error: 2.3330 Epoch 205/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.0066 - mean_absolute_error: 0.6724 - val_loss: 10.1528 - val_mean_absolute_error: 2.3004 Epoch 206/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.9779 - mean_absolute_error: 0.6938 - val_loss: 11.5405 - val_mean_absolute_error: 2.3483 Epoch 207/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.0316 - mean_absolute_error: 0.7179 - val_loss: 10.5162 - val_mean_absolute_error: 2.3436 Epoch 208/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.8511 - mean_absolute_error: 0.6315 - val_loss: 11.3080 - val_mean_absolute_error: 2.3700 Epoch 209/1000 7/7 [==============================] - 0s 24ms/step - loss: 1.0218 - mean_absolute_error: 0.6927 - val_loss: 11.2761 - val_mean_absolute_error: 2.3818 Epoch 210/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.9744 - mean_absolute_error: 0.6916 - val_loss: 10.8121 - val_mean_absolute_error: 2.4038 Epoch 211/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.9938 - mean_absolute_error: 0.7117 - val_loss: 11.8017 - val_mean_absolute_error: 2.3834 Epoch 212/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.9572 - mean_absolute_error: 0.7221 - val_loss: 10.7268 - val_mean_absolute_error: 2.3390 Epoch 213/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.7668 - mean_absolute_error: 0.6138 - val_loss: 10.5396 - val_mean_absolute_error: 2.3335 Epoch 214/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.8374 - mean_absolute_error: 0.6133 - val_loss: 11.6146 - val_mean_absolute_error: 2.3534 Epoch 215/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.7648 - mean_absolute_error: 0.6058 - val_loss: 10.4798 - val_mean_absolute_error: 2.3424 Epoch 216/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.7795 - mean_absolute_error: 0.6316 - val_loss: 11.7002 - val_mean_absolute_error: 2.4017 Epoch 217/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.7397 - mean_absolute_error: 0.6131 - val_loss: 10.5399 - val_mean_absolute_error: 2.3490 Epoch 218/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.7368 - mean_absolute_error: 0.5830 - val_loss: 11.0356 - val_mean_absolute_error: 2.3571 Epoch 219/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.7606 - mean_absolute_error: 0.5989 - val_loss: 11.2188 - val_mean_absolute_error: 2.3931 Epoch 220/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.7824 - mean_absolute_error: 0.6107 - val_loss: 10.8619 - val_mean_absolute_error: 2.3645 Epoch 221/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.6697 - mean_absolute_error: 0.5534 - val_loss: 11.0655 - val_mean_absolute_error: 2.3646 Epoch 222/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.7328 - mean_absolute_error: 0.5826 - val_loss: 12.3487 - val_mean_absolute_error: 2.4241 Epoch 223/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.7441 - mean_absolute_error: 0.6072 - val_loss: 10.2585 - val_mean_absolute_error: 2.3857 Epoch 224/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.8942 - mean_absolute_error: 0.6762 - val_loss: 13.2562 - val_mean_absolute_error: 2.4587 Epoch 225/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.8483 - mean_absolute_error: 0.6623 - val_loss: 9.4447 - val_mean_absolute_error: 2.3353 Epoch 226/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.7876 - mean_absolute_error: 0.6430 - val_loss: 13.1249 - val_mean_absolute_error: 2.4869 Epoch 227/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.9920 - mean_absolute_error: 0.7288 - val_loss: 10.8149 - val_mean_absolute_error: 2.3563 Epoch 228/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.9776 - mean_absolute_error: 0.7029 - val_loss: 10.5787 - val_mean_absolute_error: 2.3044 Epoch 229/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.9174 - mean_absolute_error: 0.6819 - val_loss: 11.1250 - val_mean_absolute_error: 2.3278 Epoch 230/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.8385 - mean_absolute_error: 0.6376 - val_loss: 11.6145 - val_mean_absolute_error: 2.4082 Epoch 231/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.8387 - mean_absolute_error: 0.6494 - val_loss: 11.4320 - val_mean_absolute_error: 2.3625 Epoch 232/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.2950 - mean_absolute_error: 0.7364 - val_loss: 10.5543 - val_mean_absolute_error: 2.3105 Epoch 233/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.0108 - mean_absolute_error: 0.6561 - val_loss: 11.6974 - val_mean_absolute_error: 2.3798 Epoch 234/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.9589 - mean_absolute_error: 0.6751 - val_loss: 10.2667 - val_mean_absolute_error: 2.3109 Epoch 235/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.9978 - mean_absolute_error: 0.6865 - val_loss: 11.7137 - val_mean_absolute_error: 2.4145 Epoch 236/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.8874 - mean_absolute_error: 0.6617 - val_loss: 11.8787 - val_mean_absolute_error: 2.4164 Epoch 237/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.8734 - mean_absolute_error: 0.6422 - val_loss: 11.5909 - val_mean_absolute_error: 2.3819 Epoch 238/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.7995 - mean_absolute_error: 0.6213 - val_loss: 11.2184 - val_mean_absolute_error: 2.4047 Epoch 239/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.9224 - mean_absolute_error: 0.6966 - val_loss: 11.5527 - val_mean_absolute_error: 2.3749 Epoch 240/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.9326 - mean_absolute_error: 0.6982 - val_loss: 11.7216 - val_mean_absolute_error: 2.4346 Epoch 241/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.8285 - mean_absolute_error: 0.6545 - val_loss: 11.4235 - val_mean_absolute_error: 2.4260 Epoch 242/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.8877 - mean_absolute_error: 0.6861 - val_loss: 10.2112 - val_mean_absolute_error: 2.3503 Epoch 243/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.7352 - mean_absolute_error: 0.6344 - val_loss: 13.3535 - val_mean_absolute_error: 2.5285 Epoch 244/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.8763 - mean_absolute_error: 0.6793 - val_loss: 10.5138 - val_mean_absolute_error: 2.3902 Epoch 245/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.7453 - mean_absolute_error: 0.6066 - val_loss: 11.6313 - val_mean_absolute_error: 2.4657 Epoch 246/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.7740 - mean_absolute_error: 0.6507 - val_loss: 12.2183 - val_mean_absolute_error: 2.3939 Epoch 247/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.7669 - mean_absolute_error: 0.6366 - val_loss: 10.3696 - val_mean_absolute_error: 2.3620 Epoch 248/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.8221 - mean_absolute_error: 0.6537 - val_loss: 11.7141 - val_mean_absolute_error: 2.3773 Epoch 249/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.6819 - mean_absolute_error: 0.5667 - val_loss: 11.3776 - val_mean_absolute_error: 2.3887 Epoch 250/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.6643 - mean_absolute_error: 0.5662 - val_loss: 10.7070 - val_mean_absolute_error: 2.3462 Epoch 251/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.6068 - mean_absolute_error: 0.5246 - val_loss: 11.6584 - val_mean_absolute_error: 2.4105 Epoch 252/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.5644 - mean_absolute_error: 0.5005 - val_loss: 10.4692 - val_mean_absolute_error: 2.3279 Epoch 253/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5516 - mean_absolute_error: 0.4890 - val_loss: 11.7056 - val_mean_absolute_error: 2.3726 Epoch 254/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.5933 - mean_absolute_error: 0.5034 - val_loss: 11.1393 - val_mean_absolute_error: 2.3806 Epoch 255/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5704 - mean_absolute_error: 0.5158 - val_loss: 11.3937 - val_mean_absolute_error: 2.3732 Epoch 256/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5451 - mean_absolute_error: 0.4960 - val_loss: 11.5957 - val_mean_absolute_error: 2.3803 Epoch 257/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5729 - mean_absolute_error: 0.5138 - val_loss: 11.0876 - val_mean_absolute_error: 2.3929 Epoch 258/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5886 - mean_absolute_error: 0.4906 - val_loss: 11.0813 - val_mean_absolute_error: 2.3754 Epoch 259/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.5567 - mean_absolute_error: 0.4914 - val_loss: 11.4910 - val_mean_absolute_error: 2.3417 Epoch 260/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5455 - mean_absolute_error: 0.4947 - val_loss: 11.0118 - val_mean_absolute_error: 2.3887 Epoch 261/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.5677 - mean_absolute_error: 0.4988 - val_loss: 11.1491 - val_mean_absolute_error: 2.3690 Epoch 262/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5089 - mean_absolute_error: 0.4851 - val_loss: 11.7201 - val_mean_absolute_error: 2.4086 Epoch 263/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.5551 - mean_absolute_error: 0.4951 - val_loss: 11.2738 - val_mean_absolute_error: 2.3933 Epoch 264/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.5591 - mean_absolute_error: 0.4885 - val_loss: 11.9995 - val_mean_absolute_error: 2.3939 Epoch 265/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.5210 - mean_absolute_error: 0.4859 - val_loss: 11.1052 - val_mean_absolute_error: 2.3610 Epoch 266/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.5464 - mean_absolute_error: 0.4674 - val_loss: 11.7420 - val_mean_absolute_error: 2.3572 Epoch 267/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.5365 - mean_absolute_error: 0.4891 - val_loss: 11.4066 - val_mean_absolute_error: 2.3907 Epoch 268/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.5525 - mean_absolute_error: 0.4881 - val_loss: 11.1991 - val_mean_absolute_error: 2.4205 Epoch 269/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.5601 - mean_absolute_error: 0.4878 - val_loss: 11.5078 - val_mean_absolute_error: 2.4029 Epoch 270/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.5117 - mean_absolute_error: 0.4644 - val_loss: 10.9876 - val_mean_absolute_error: 2.3552 Epoch 271/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.5297 - mean_absolute_error: 0.4482 - val_loss: 11.5248 - val_mean_absolute_error: 2.4332 Epoch 272/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.5703 - mean_absolute_error: 0.4953 - val_loss: 11.5160 - val_mean_absolute_error: 2.3818 Epoch 273/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.5175 - mean_absolute_error: 0.4775 - val_loss: 11.4383 - val_mean_absolute_error: 2.4076 Epoch 274/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.6159 - mean_absolute_error: 0.5189 - val_loss: 11.4738 - val_mean_absolute_error: 2.3877 Epoch 275/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.5882 - mean_absolute_error: 0.5401 - val_loss: 11.2920 - val_mean_absolute_error: 2.4213 Epoch 276/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.6668 - mean_absolute_error: 0.5687 - val_loss: 11.8438 - val_mean_absolute_error: 2.3851 Epoch 277/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.6864 - mean_absolute_error: 0.5692 - val_loss: 11.3314 - val_mean_absolute_error: 2.3892 Epoch 278/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.6809 - mean_absolute_error: 0.5407 - val_loss: 12.5064 - val_mean_absolute_error: 2.4230 Epoch 279/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.8419 - mean_absolute_error: 0.6385 - val_loss: 10.5786 - val_mean_absolute_error: 2.3774 Epoch 280/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.8016 - mean_absolute_error: 0.6318 - val_loss: 12.0430 - val_mean_absolute_error: 2.4609 Epoch 281/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.7531 - mean_absolute_error: 0.6055 - val_loss: 11.3457 - val_mean_absolute_error: 2.3972 Epoch 282/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.5539 - mean_absolute_error: 0.4995 - val_loss: 11.2367 - val_mean_absolute_error: 2.3344 Epoch 283/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.5156 - mean_absolute_error: 0.4815 - val_loss: 11.3140 - val_mean_absolute_error: 2.3879 Epoch 284/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.5235 - mean_absolute_error: 0.4722 - val_loss: 11.0977 - val_mean_absolute_error: 2.3688 Epoch 285/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5788 - mean_absolute_error: 0.5299 - val_loss: 11.8272 - val_mean_absolute_error: 2.4873 Epoch 286/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5484 - mean_absolute_error: 0.5040 - val_loss: 12.0022 - val_mean_absolute_error: 2.3902 Epoch 287/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.7212 - mean_absolute_error: 0.6028 - val_loss: 10.4255 - val_mean_absolute_error: 2.3626 Epoch 288/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.0294 - mean_absolute_error: 0.7520 - val_loss: 14.2674 - val_mean_absolute_error: 2.5792 Epoch 289/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.0809 - mean_absolute_error: 0.7681 - val_loss: 10.9145 - val_mean_absolute_error: 2.3512 Epoch 290/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.8108 - mean_absolute_error: 0.6155 - val_loss: 10.9787 - val_mean_absolute_error: 2.3796 Epoch 291/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.6081 - mean_absolute_error: 0.5289 - val_loss: 12.0920 - val_mean_absolute_error: 2.4600 Epoch 292/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.7104 - mean_absolute_error: 0.5584 - val_loss: 11.4036 - val_mean_absolute_error: 2.4487 Epoch 293/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5237 - mean_absolute_error: 0.4879 - val_loss: 11.9810 - val_mean_absolute_error: 2.4054 Epoch 294/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5486 - mean_absolute_error: 0.4952 - val_loss: 11.2963 - val_mean_absolute_error: 2.4158 Epoch 295/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.6369 - mean_absolute_error: 0.5365 - val_loss: 10.7507 - val_mean_absolute_error: 2.3258 Epoch 296/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5360 - mean_absolute_error: 0.5006 - val_loss: 10.7204 - val_mean_absolute_error: 2.3196 Epoch 297/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4910 - mean_absolute_error: 0.4757 - val_loss: 12.1047 - val_mean_absolute_error: 2.4641 Epoch 298/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.5380 - mean_absolute_error: 0.5020 - val_loss: 11.0781 - val_mean_absolute_error: 2.4766 Epoch 299/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.5367 - mean_absolute_error: 0.4755 - val_loss: 11.5423 - val_mean_absolute_error: 2.3980 Epoch 300/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5091 - mean_absolute_error: 0.4583 - val_loss: 11.7307 - val_mean_absolute_error: 2.3859 Epoch 301/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.4826 - mean_absolute_error: 0.4632 - val_loss: 11.3184 - val_mean_absolute_error: 2.4282 Epoch 302/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4443 - mean_absolute_error: 0.4123 - val_loss: 11.7519 - val_mean_absolute_error: 2.4301 Epoch 303/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.4531 - mean_absolute_error: 0.4001 - val_loss: 11.5104 - val_mean_absolute_error: 2.4074 Epoch 304/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.4182 - mean_absolute_error: 0.3851 - val_loss: 11.4232 - val_mean_absolute_error: 2.4350 Epoch 305/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3715 - mean_absolute_error: 0.3616 - val_loss: 11.4703 - val_mean_absolute_error: 2.4315 Epoch 306/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.4242 - mean_absolute_error: 0.3754 - val_loss: 11.4124 - val_mean_absolute_error: 2.4376 Epoch 307/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3753 - mean_absolute_error: 0.3684 - val_loss: 11.5155 - val_mean_absolute_error: 2.4180 Epoch 308/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.3532 - mean_absolute_error: 0.3327 - val_loss: 11.2741 - val_mean_absolute_error: 2.3979 Epoch 309/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4292 - mean_absolute_error: 0.3930 - val_loss: 11.4567 - val_mean_absolute_error: 2.4137 Epoch 310/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4714 - mean_absolute_error: 0.4272 - val_loss: 10.9603 - val_mean_absolute_error: 2.3703 Epoch 311/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.4604 - mean_absolute_error: 0.4350 - val_loss: 12.0402 - val_mean_absolute_error: 2.4925 Epoch 312/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4260 - mean_absolute_error: 0.4121 - val_loss: 12.0787 - val_mean_absolute_error: 2.4256 Epoch 313/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.4004 - mean_absolute_error: 0.4063 - val_loss: 10.6818 - val_mean_absolute_error: 2.4335 Epoch 314/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4157 - mean_absolute_error: 0.4041 - val_loss: 11.5595 - val_mean_absolute_error: 2.4414 Epoch 315/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4830 - mean_absolute_error: 0.4383 - val_loss: 12.0514 - val_mean_absolute_error: 2.4207 Epoch 316/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.4554 - mean_absolute_error: 0.4486 - val_loss: 10.7241 - val_mean_absolute_error: 2.3999 Epoch 317/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.5685 - mean_absolute_error: 0.4704 - val_loss: 11.0345 - val_mean_absolute_error: 2.3548 Epoch 318/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.4430 - mean_absolute_error: 0.4365 - val_loss: 11.7121 - val_mean_absolute_error: 2.4449 Epoch 319/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4963 - mean_absolute_error: 0.4565 - val_loss: 11.6632 - val_mean_absolute_error: 2.5158 Epoch 320/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.4974 - mean_absolute_error: 0.4604 - val_loss: 11.7648 - val_mean_absolute_error: 2.5413 Epoch 321/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.5182 - mean_absolute_error: 0.4607 - val_loss: 10.9871 - val_mean_absolute_error: 2.3365 Epoch 322/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.4913 - mean_absolute_error: 0.4836 - val_loss: 10.3400 - val_mean_absolute_error: 2.4562 Epoch 323/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.4643 - mean_absolute_error: 0.4552 - val_loss: 12.3932 - val_mean_absolute_error: 2.5088 Epoch 324/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4633 - mean_absolute_error: 0.4484 - val_loss: 11.3897 - val_mean_absolute_error: 2.4169 Epoch 325/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.3657 - mean_absolute_error: 0.3574 - val_loss: 11.4551 - val_mean_absolute_error: 2.4530 Epoch 326/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3540 - mean_absolute_error: 0.3644 - val_loss: 11.6783 - val_mean_absolute_error: 2.4756 Epoch 327/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4196 - mean_absolute_error: 0.3946 - val_loss: 11.0940 - val_mean_absolute_error: 2.4543 Epoch 328/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3810 - mean_absolute_error: 0.3811 - val_loss: 11.7823 - val_mean_absolute_error: 2.4387 Epoch 329/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.4215 - mean_absolute_error: 0.3914 - val_loss: 11.2040 - val_mean_absolute_error: 2.4042 Epoch 330/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.4348 - mean_absolute_error: 0.4367 - val_loss: 11.6861 - val_mean_absolute_error: 2.4418 Epoch 331/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.4674 - mean_absolute_error: 0.4532 - val_loss: 11.6167 - val_mean_absolute_error: 2.3937 Epoch 332/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4979 - mean_absolute_error: 0.4872 - val_loss: 10.8883 - val_mean_absolute_error: 2.4425 Epoch 333/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4936 - mean_absolute_error: 0.4602 - val_loss: 12.4558 - val_mean_absolute_error: 2.4434 Epoch 334/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.6286 - mean_absolute_error: 0.5422 - val_loss: 11.5065 - val_mean_absolute_error: 2.4772 Epoch 335/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.6856 - mean_absolute_error: 0.5559 - val_loss: 11.5429 - val_mean_absolute_error: 2.4003 Epoch 336/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5267 - mean_absolute_error: 0.5030 - val_loss: 11.0380 - val_mean_absolute_error: 2.4157 Epoch 337/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5082 - mean_absolute_error: 0.4797 - val_loss: 11.5741 - val_mean_absolute_error: 2.4663 Epoch 338/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4798 - mean_absolute_error: 0.4565 - val_loss: 12.3408 - val_mean_absolute_error: 2.5039 Epoch 339/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5345 - mean_absolute_error: 0.5004 - val_loss: 11.3730 - val_mean_absolute_error: 2.5174 Epoch 340/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4773 - mean_absolute_error: 0.4755 - val_loss: 11.9888 - val_mean_absolute_error: 2.4190 Epoch 341/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4961 - mean_absolute_error: 0.5008 - val_loss: 11.2682 - val_mean_absolute_error: 2.4227 Epoch 342/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4296 - mean_absolute_error: 0.4347 - val_loss: 10.4518 - val_mean_absolute_error: 2.3275 Epoch 343/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4061 - mean_absolute_error: 0.4258 - val_loss: 11.9637 - val_mean_absolute_error: 2.4328 Epoch 344/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.4335 - mean_absolute_error: 0.4202 - val_loss: 11.5839 - val_mean_absolute_error: 2.4083 Epoch 345/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3631 - mean_absolute_error: 0.3714 - val_loss: 11.1468 - val_mean_absolute_error: 2.3655 Epoch 346/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3899 - mean_absolute_error: 0.4051 - val_loss: 10.9427 - val_mean_absolute_error: 2.4056 Epoch 347/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3553 - mean_absolute_error: 0.3824 - val_loss: 11.1364 - val_mean_absolute_error: 2.4169 Epoch 348/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.3845 - mean_absolute_error: 0.3977 - val_loss: 11.3469 - val_mean_absolute_error: 2.3815 Epoch 349/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.4933 - mean_absolute_error: 0.4522 - val_loss: 11.2059 - val_mean_absolute_error: 2.4351 Epoch 350/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5541 - mean_absolute_error: 0.4674 - val_loss: 12.2018 - val_mean_absolute_error: 2.4542 Epoch 351/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.6168 - mean_absolute_error: 0.4913 - val_loss: 10.5841 - val_mean_absolute_error: 2.3860 Epoch 352/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.6176 - mean_absolute_error: 0.5298 - val_loss: 11.4075 - val_mean_absolute_error: 2.4567 Epoch 353/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.6048 - mean_absolute_error: 0.5118 - val_loss: 11.4411 - val_mean_absolute_error: 2.4243 Epoch 354/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.6805 - mean_absolute_error: 0.5179 - val_loss: 10.8632 - val_mean_absolute_error: 2.4376 Epoch 355/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.4256 - mean_absolute_error: 0.4540 - val_loss: 11.3947 - val_mean_absolute_error: 2.3953 Epoch 356/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.7310 - mean_absolute_error: 0.5472 - val_loss: 11.1666 - val_mean_absolute_error: 2.3774 Epoch 357/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.7117 - mean_absolute_error: 0.5753 - val_loss: 10.5130 - val_mean_absolute_error: 2.3363 Epoch 358/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.6541 - mean_absolute_error: 0.5556 - val_loss: 12.4806 - val_mean_absolute_error: 2.4348 Epoch 359/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.6038 - mean_absolute_error: 0.5067 - val_loss: 11.7450 - val_mean_absolute_error: 2.4953 Epoch 360/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.6527 - mean_absolute_error: 0.5136 - val_loss: 11.5350 - val_mean_absolute_error: 2.3760 Epoch 361/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.5572 - mean_absolute_error: 0.4783 - val_loss: 11.4498 - val_mean_absolute_error: 2.4289 Epoch 362/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.5464 - mean_absolute_error: 0.5037 - val_loss: 10.9167 - val_mean_absolute_error: 2.3598 Epoch 363/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.4957 - mean_absolute_error: 0.4587 - val_loss: 11.6033 - val_mean_absolute_error: 2.3440 Epoch 364/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4894 - mean_absolute_error: 0.5000 - val_loss: 11.3201 - val_mean_absolute_error: 2.4333 Epoch 365/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4465 - mean_absolute_error: 0.4505 - val_loss: 11.4252 - val_mean_absolute_error: 2.3089 Epoch 366/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4521 - mean_absolute_error: 0.4316 - val_loss: 11.7963 - val_mean_absolute_error: 2.4411 Epoch 367/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.4208 - mean_absolute_error: 0.4248 - val_loss: 11.0447 - val_mean_absolute_error: 2.3815 Epoch 368/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3431 - mean_absolute_error: 0.3751 - val_loss: 11.1455 - val_mean_absolute_error: 2.4061 Epoch 369/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.3474 - mean_absolute_error: 0.3727 - val_loss: 12.4181 - val_mean_absolute_error: 2.4868 Epoch 370/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.4040 - mean_absolute_error: 0.3971 - val_loss: 10.3001 - val_mean_absolute_error: 2.3777 Epoch 371/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.3355 - mean_absolute_error: 0.3799 - val_loss: 11.5526 - val_mean_absolute_error: 2.4166 Epoch 372/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.3537 - mean_absolute_error: 0.3728 - val_loss: 11.5257 - val_mean_absolute_error: 2.4552 Epoch 373/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.3280 - mean_absolute_error: 0.3405 - val_loss: 11.7271 - val_mean_absolute_error: 2.4397 Epoch 374/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.3180 - mean_absolute_error: 0.3516 - val_loss: 10.9579 - val_mean_absolute_error: 2.3958 Epoch 375/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2959 - mean_absolute_error: 0.3152 - val_loss: 10.6487 - val_mean_absolute_error: 2.3868 Epoch 376/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2915 - mean_absolute_error: 0.3109 - val_loss: 11.5236 - val_mean_absolute_error: 2.3994 Epoch 377/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2786 - mean_absolute_error: 0.2984 - val_loss: 11.8209 - val_mean_absolute_error: 2.4165 Epoch 378/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3385 - mean_absolute_error: 0.3512 - val_loss: 10.9464 - val_mean_absolute_error: 2.3976 Epoch 379/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3282 - mean_absolute_error: 0.3581 - val_loss: 11.4201 - val_mean_absolute_error: 2.3843 Epoch 380/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4043 - mean_absolute_error: 0.4195 - val_loss: 11.0619 - val_mean_absolute_error: 2.4347 Epoch 381/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5052 - mean_absolute_error: 0.4805 - val_loss: 11.3217 - val_mean_absolute_error: 2.3383 Epoch 382/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.4951 - mean_absolute_error: 0.4503 - val_loss: 11.5898 - val_mean_absolute_error: 2.4464 Epoch 383/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.4769 - mean_absolute_error: 0.4613 - val_loss: 11.6396 - val_mean_absolute_error: 2.4075 Epoch 384/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.4674 - mean_absolute_error: 0.4636 - val_loss: 11.1625 - val_mean_absolute_error: 2.4573 Epoch 385/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.5351 - mean_absolute_error: 0.4729 - val_loss: 10.9256 - val_mean_absolute_error: 2.3613 Epoch 386/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.4533 - mean_absolute_error: 0.4327 - val_loss: 12.3233 - val_mean_absolute_error: 2.4766 Epoch 387/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.4315 - mean_absolute_error: 0.4543 - val_loss: 10.5881 - val_mean_absolute_error: 2.3698 Epoch 388/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3731 - mean_absolute_error: 0.4055 - val_loss: 11.4410 - val_mean_absolute_error: 2.4394 Epoch 389/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.3321 - mean_absolute_error: 0.3663 - val_loss: 11.5417 - val_mean_absolute_error: 2.4327 Epoch 390/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2975 - mean_absolute_error: 0.3409 - val_loss: 11.0750 - val_mean_absolute_error: 2.4346 Epoch 391/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2806 - mean_absolute_error: 0.3042 - val_loss: 11.7707 - val_mean_absolute_error: 2.4262 Epoch 392/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2744 - mean_absolute_error: 0.3039 - val_loss: 11.0971 - val_mean_absolute_error: 2.4108 Epoch 393/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2796 - mean_absolute_error: 0.2861 - val_loss: 10.5036 - val_mean_absolute_error: 2.3630 Epoch 394/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2704 - mean_absolute_error: 0.3165 - val_loss: 11.3393 - val_mean_absolute_error: 2.4163 Epoch 395/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2547 - mean_absolute_error: 0.2878 - val_loss: 11.2621 - val_mean_absolute_error: 2.3625 Epoch 396/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3254 - mean_absolute_error: 0.3612 - val_loss: 11.0107 - val_mean_absolute_error: 2.4294 Epoch 397/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3456 - mean_absolute_error: 0.3492 - val_loss: 11.4579 - val_mean_absolute_error: 2.4197 Epoch 398/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.3094 - mean_absolute_error: 0.3473 - val_loss: 11.0905 - val_mean_absolute_error: 2.4055 Epoch 399/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2618 - mean_absolute_error: 0.3037 - val_loss: 10.4984 - val_mean_absolute_error: 2.4045 Epoch 400/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2673 - mean_absolute_error: 0.3003 - val_loss: 12.6898 - val_mean_absolute_error: 2.5073 Epoch 401/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2950 - mean_absolute_error: 0.3232 - val_loss: 11.2901 - val_mean_absolute_error: 2.4352 Epoch 402/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2723 - mean_absolute_error: 0.3062 - val_loss: 11.0828 - val_mean_absolute_error: 2.4259 Epoch 403/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.2745 - mean_absolute_error: 0.3190 - val_loss: 10.8430 - val_mean_absolute_error: 2.4255 Epoch 404/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2542 - mean_absolute_error: 0.2943 - val_loss: 12.0954 - val_mean_absolute_error: 2.4392 Epoch 405/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2601 - mean_absolute_error: 0.3091 - val_loss: 11.0178 - val_mean_absolute_error: 2.3886 Epoch 406/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2743 - mean_absolute_error: 0.3199 - val_loss: 11.2290 - val_mean_absolute_error: 2.4358 Epoch 407/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3483 - mean_absolute_error: 0.3007 - val_loss: 11.4351 - val_mean_absolute_error: 2.4601 Epoch 408/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.3337 - mean_absolute_error: 0.3348 - val_loss: 11.9482 - val_mean_absolute_error: 2.4819 Epoch 409/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.3785 - mean_absolute_error: 0.4159 - val_loss: 10.8934 - val_mean_absolute_error: 2.3683 Epoch 410/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.2863 - mean_absolute_error: 0.3381 - val_loss: 10.9836 - val_mean_absolute_error: 2.4051 Epoch 411/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.2687 - mean_absolute_error: 0.3150 - val_loss: 11.1602 - val_mean_absolute_error: 2.4255 Epoch 412/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.2987 - mean_absolute_error: 0.3148 - val_loss: 11.2066 - val_mean_absolute_error: 2.4034 Epoch 413/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.3875 - mean_absolute_error: 0.3713 - val_loss: 11.1264 - val_mean_absolute_error: 2.4205 Epoch 414/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.3965 - mean_absolute_error: 0.3606 - val_loss: 11.2928 - val_mean_absolute_error: 2.3812 Epoch 415/1000 7/7 [==============================] - 0s 46ms/step - loss: 0.3210 - mean_absolute_error: 0.3325 - val_loss: 11.4350 - val_mean_absolute_error: 2.4837 Epoch 416/1000 7/7 [==============================] - 0s 42ms/step - loss: 0.3056 - mean_absolute_error: 0.3349 - val_loss: 11.5586 - val_mean_absolute_error: 2.4484 Epoch 417/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.3456 - mean_absolute_error: 0.3627 - val_loss: 10.5583 - val_mean_absolute_error: 2.4038 Epoch 418/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3002 - mean_absolute_error: 0.3501 - val_loss: 11.2324 - val_mean_absolute_error: 2.4054 Epoch 419/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2878 - mean_absolute_error: 0.3239 - val_loss: 11.6943 - val_mean_absolute_error: 2.4646 Epoch 420/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3550 - mean_absolute_error: 0.3873 - val_loss: 11.4449 - val_mean_absolute_error: 2.4108 Epoch 421/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3579 - mean_absolute_error: 0.3887 - val_loss: 10.9414 - val_mean_absolute_error: 2.4580 Epoch 422/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.3243 - mean_absolute_error: 0.3653 - val_loss: 11.4999 - val_mean_absolute_error: 2.4128 Epoch 423/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.3554 - mean_absolute_error: 0.4015 - val_loss: 11.6130 - val_mean_absolute_error: 2.4420 Epoch 424/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3526 - mean_absolute_error: 0.3979 - val_loss: 10.8700 - val_mean_absolute_error: 2.3943 Epoch 425/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3468 - mean_absolute_error: 0.3838 - val_loss: 11.9776 - val_mean_absolute_error: 2.4657 Epoch 426/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3047 - mean_absolute_error: 0.3616 - val_loss: 11.0298 - val_mean_absolute_error: 2.3953 Epoch 427/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2908 - mean_absolute_error: 0.3227 - val_loss: 11.5800 - val_mean_absolute_error: 2.4712 Epoch 428/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.3131 - mean_absolute_error: 0.3511 - val_loss: 10.8881 - val_mean_absolute_error: 2.3807 Epoch 429/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3524 - mean_absolute_error: 0.3910 - val_loss: 10.6831 - val_mean_absolute_error: 2.4108 Epoch 430/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.4244 - mean_absolute_error: 0.4170 - val_loss: 11.3497 - val_mean_absolute_error: 2.3713 Epoch 431/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3796 - mean_absolute_error: 0.4229 - val_loss: 12.4195 - val_mean_absolute_error: 2.4913 Epoch 432/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.4793 - mean_absolute_error: 0.4745 - val_loss: 10.9589 - val_mean_absolute_error: 2.4765 Epoch 433/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.4958 - mean_absolute_error: 0.4816 - val_loss: 12.0481 - val_mean_absolute_error: 2.4951 Epoch 434/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3934 - mean_absolute_error: 0.4438 - val_loss: 12.6398 - val_mean_absolute_error: 2.4507 Epoch 435/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5230 - mean_absolute_error: 0.4863 - val_loss: 10.6652 - val_mean_absolute_error: 2.4363 Epoch 436/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.4541 - mean_absolute_error: 0.4781 - val_loss: 10.8244 - val_mean_absolute_error: 2.3871 Epoch 437/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.4945 - mean_absolute_error: 0.4637 - val_loss: 10.7797 - val_mean_absolute_error: 2.3891 Epoch 438/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.4491 - mean_absolute_error: 0.4760 - val_loss: 11.4457 - val_mean_absolute_error: 2.4500 Epoch 439/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.4389 - mean_absolute_error: 0.4436 - val_loss: 11.6313 - val_mean_absolute_error: 2.5263 Epoch 440/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.4304 - mean_absolute_error: 0.4102 - val_loss: 11.3471 - val_mean_absolute_error: 2.4422 Epoch 441/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.3893 - mean_absolute_error: 0.4143 - val_loss: 11.0962 - val_mean_absolute_error: 2.3339 Epoch 442/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3528 - mean_absolute_error: 0.4096 - val_loss: 10.4346 - val_mean_absolute_error: 2.3480 Epoch 443/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2951 - mean_absolute_error: 0.3321 - val_loss: 10.8426 - val_mean_absolute_error: 2.3635 Epoch 444/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2515 - mean_absolute_error: 0.3143 - val_loss: 10.9943 - val_mean_absolute_error: 2.3716 Epoch 445/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3019 - mean_absolute_error: 0.3476 - val_loss: 10.7065 - val_mean_absolute_error: 2.3767 Epoch 446/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2480 - mean_absolute_error: 0.3102 - val_loss: 11.1834 - val_mean_absolute_error: 2.3833 Epoch 447/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2703 - mean_absolute_error: 0.3125 - val_loss: 11.0339 - val_mean_absolute_error: 2.4033 Epoch 448/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2485 - mean_absolute_error: 0.3047 - val_loss: 10.7502 - val_mean_absolute_error: 2.4055 Epoch 449/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2331 - mean_absolute_error: 0.2835 - val_loss: 11.7565 - val_mean_absolute_error: 2.4402 Epoch 450/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2302 - mean_absolute_error: 0.2915 - val_loss: 10.6879 - val_mean_absolute_error: 2.3667 Epoch 451/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.2575 - mean_absolute_error: 0.3008 - val_loss: 11.1571 - val_mean_absolute_error: 2.3860 Epoch 452/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3191 - mean_absolute_error: 0.3368 - val_loss: 10.8119 - val_mean_absolute_error: 2.3956 Epoch 453/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.3369 - mean_absolute_error: 0.3868 - val_loss: 11.4559 - val_mean_absolute_error: 2.4103 Epoch 454/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2988 - mean_absolute_error: 0.3438 - val_loss: 10.9658 - val_mean_absolute_error: 2.4179 Epoch 455/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2445 - mean_absolute_error: 0.3019 - val_loss: 10.5141 - val_mean_absolute_error: 2.3495 Epoch 456/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2187 - mean_absolute_error: 0.2878 - val_loss: 10.8835 - val_mean_absolute_error: 2.3785 Epoch 457/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2088 - mean_absolute_error: 0.2501 - val_loss: 10.9730 - val_mean_absolute_error: 2.3922 Epoch 458/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2449 - mean_absolute_error: 0.2862 - val_loss: 11.1979 - val_mean_absolute_error: 2.4425 Epoch 459/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2486 - mean_absolute_error: 0.3217 - val_loss: 10.9623 - val_mean_absolute_error: 2.3785 Epoch 460/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2655 - mean_absolute_error: 0.3205 - val_loss: 10.8356 - val_mean_absolute_error: 2.4118 Epoch 461/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2673 - mean_absolute_error: 0.3108 - val_loss: 10.9911 - val_mean_absolute_error: 2.4149 Epoch 462/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2634 - mean_absolute_error: 0.3070 - val_loss: 11.8809 - val_mean_absolute_error: 2.4403 Epoch 463/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2233 - mean_absolute_error: 0.3253 - val_loss: 10.3207 - val_mean_absolute_error: 2.3892 Epoch 464/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3499 - mean_absolute_error: 0.3870 - val_loss: 11.3842 - val_mean_absolute_error: 2.4285 Epoch 465/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2714 - mean_absolute_error: 0.3115 - val_loss: 11.0138 - val_mean_absolute_error: 2.4292 Epoch 466/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2496 - mean_absolute_error: 0.3044 - val_loss: 10.7433 - val_mean_absolute_error: 2.3913 Epoch 467/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2505 - mean_absolute_error: 0.3080 - val_loss: 10.6921 - val_mean_absolute_error: 2.3868 Epoch 468/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2177 - mean_absolute_error: 0.2795 - val_loss: 11.3918 - val_mean_absolute_error: 2.4245 Epoch 469/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2186 - mean_absolute_error: 0.2903 - val_loss: 10.6068 - val_mean_absolute_error: 2.3629 Epoch 470/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2308 - mean_absolute_error: 0.3094 - val_loss: 10.4585 - val_mean_absolute_error: 2.4104 Epoch 471/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2410 - mean_absolute_error: 0.3197 - val_loss: 11.1824 - val_mean_absolute_error: 2.3972 Epoch 472/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2591 - mean_absolute_error: 0.3413 - val_loss: 10.8990 - val_mean_absolute_error: 2.4190 Epoch 473/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.3700 - mean_absolute_error: 0.4248 - val_loss: 10.3245 - val_mean_absolute_error: 2.3382 Epoch 474/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4597 - mean_absolute_error: 0.5023 - val_loss: 11.3989 - val_mean_absolute_error: 2.4428 Epoch 475/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.4995 - mean_absolute_error: 0.5349 - val_loss: 11.7424 - val_mean_absolute_error: 2.4074 Epoch 476/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.5943 - mean_absolute_error: 0.5040 - val_loss: 10.4811 - val_mean_absolute_error: 2.3259 Epoch 477/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.3776 - mean_absolute_error: 0.4322 - val_loss: 10.2311 - val_mean_absolute_error: 2.3991 Epoch 478/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.5794 - mean_absolute_error: 0.5245 - val_loss: 11.3172 - val_mean_absolute_error: 2.3519 Epoch 479/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.5610 - mean_absolute_error: 0.5078 - val_loss: 11.3596 - val_mean_absolute_error: 2.4231 Epoch 480/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.6218 - mean_absolute_error: 0.5574 - val_loss: 11.7683 - val_mean_absolute_error: 2.5015 Epoch 481/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5077 - mean_absolute_error: 0.5431 - val_loss: 10.2371 - val_mean_absolute_error: 2.3717 Epoch 482/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.4642 - mean_absolute_error: 0.4809 - val_loss: 10.7639 - val_mean_absolute_error: 2.4091 Epoch 483/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.4266 - mean_absolute_error: 0.4757 - val_loss: 11.2383 - val_mean_absolute_error: 2.4401 Epoch 484/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.3289 - mean_absolute_error: 0.4115 - val_loss: 11.6744 - val_mean_absolute_error: 2.4729 Epoch 485/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.2509 - mean_absolute_error: 0.3357 - val_loss: 11.1153 - val_mean_absolute_error: 2.4385 Epoch 486/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.2024 - mean_absolute_error: 0.2923 - val_loss: 11.2780 - val_mean_absolute_error: 2.4334 Epoch 487/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2661 - mean_absolute_error: 0.3363 - val_loss: 11.5768 - val_mean_absolute_error: 2.4938 Epoch 488/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2738 - mean_absolute_error: 0.3323 - val_loss: 10.4752 - val_mean_absolute_error: 2.3874 Epoch 489/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2605 - mean_absolute_error: 0.3567 - val_loss: 11.1890 - val_mean_absolute_error: 2.4792 Epoch 490/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2568 - mean_absolute_error: 0.3167 - val_loss: 11.6768 - val_mean_absolute_error: 2.4557 Epoch 491/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2807 - mean_absolute_error: 0.3554 - val_loss: 10.4664 - val_mean_absolute_error: 2.3817 Epoch 492/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.5132 - mean_absolute_error: 0.4691 - val_loss: 10.7737 - val_mean_absolute_error: 2.4251 Epoch 493/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.1414 - mean_absolute_error: 0.6213 - val_loss: 10.0009 - val_mean_absolute_error: 2.2913 Epoch 494/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.2965 - mean_absolute_error: 0.6682 - val_loss: 10.7657 - val_mean_absolute_error: 2.3128 Epoch 495/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.7426 - mean_absolute_error: 0.5946 - val_loss: 10.5852 - val_mean_absolute_error: 2.4677 Epoch 496/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.2347 - mean_absolute_error: 0.6845 - val_loss: 13.0383 - val_mean_absolute_error: 2.6146 Epoch 497/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.0677 - mean_absolute_error: 0.6965 - val_loss: 10.3251 - val_mean_absolute_error: 2.3729 Epoch 498/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.6174 - mean_absolute_error: 0.5545 - val_loss: 10.5347 - val_mean_absolute_error: 2.3208 Epoch 499/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5709 - mean_absolute_error: 0.4731 - val_loss: 11.0227 - val_mean_absolute_error: 2.4749 Epoch 500/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.5928 - mean_absolute_error: 0.4941 - val_loss: 10.8545 - val_mean_absolute_error: 2.4753 Epoch 501/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.6578 - mean_absolute_error: 0.4706 - val_loss: 10.6884 - val_mean_absolute_error: 2.4361 Epoch 502/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.3514 - mean_absolute_error: 0.3761 - val_loss: 11.1258 - val_mean_absolute_error: 2.4390 Epoch 503/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2514 - mean_absolute_error: 0.3356 - val_loss: 11.1195 - val_mean_absolute_error: 2.4863 Epoch 504/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2671 - mean_absolute_error: 0.3320 - val_loss: 11.0305 - val_mean_absolute_error: 2.4513 Epoch 505/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2320 - mean_absolute_error: 0.3169 - val_loss: 10.5155 - val_mean_absolute_error: 2.4560 Epoch 506/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.2125 - mean_absolute_error: 0.2766 - val_loss: 10.8594 - val_mean_absolute_error: 2.4171 Epoch 507/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1808 - mean_absolute_error: 0.2515 - val_loss: 11.3981 - val_mean_absolute_error: 2.4492 Epoch 508/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2020 - mean_absolute_error: 0.2683 - val_loss: 10.7500 - val_mean_absolute_error: 2.4361 Epoch 509/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1623 - mean_absolute_error: 0.2226 - val_loss: 10.8058 - val_mean_absolute_error: 2.4585 Epoch 510/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1987 - mean_absolute_error: 0.2485 - val_loss: 11.5932 - val_mean_absolute_error: 2.4611 Epoch 511/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1668 - mean_absolute_error: 0.2423 - val_loss: 10.6005 - val_mean_absolute_error: 2.4158 Epoch 512/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2096 - mean_absolute_error: 0.2663 - val_loss: 11.1027 - val_mean_absolute_error: 2.4869 Epoch 513/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1855 - mean_absolute_error: 0.2710 - val_loss: 11.7942 - val_mean_absolute_error: 2.4967 Epoch 514/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2016 - mean_absolute_error: 0.2651 - val_loss: 10.6561 - val_mean_absolute_error: 2.4307 Epoch 515/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2128 - mean_absolute_error: 0.2816 - val_loss: 10.8392 - val_mean_absolute_error: 2.4147 Epoch 516/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2578 - mean_absolute_error: 0.2864 - val_loss: 11.0293 - val_mean_absolute_error: 2.4219 Epoch 517/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2459 - mean_absolute_error: 0.3032 - val_loss: 10.5899 - val_mean_absolute_error: 2.3986 Epoch 518/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1938 - mean_absolute_error: 0.2880 - val_loss: 10.7083 - val_mean_absolute_error: 2.3718 Epoch 519/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1978 - mean_absolute_error: 0.2470 - val_loss: 10.6944 - val_mean_absolute_error: 2.4284 Epoch 520/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2449 - mean_absolute_error: 0.2992 - val_loss: 10.8029 - val_mean_absolute_error: 2.4218 Epoch 521/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3043 - mean_absolute_error: 0.2851 - val_loss: 10.5553 - val_mean_absolute_error: 2.3780 Epoch 522/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2401 - mean_absolute_error: 0.2500 - val_loss: 10.6515 - val_mean_absolute_error: 2.4264 Epoch 523/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2676 - mean_absolute_error: 0.2670 - val_loss: 11.0529 - val_mean_absolute_error: 2.4669 Epoch 524/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2145 - mean_absolute_error: 0.2372 - val_loss: 10.6483 - val_mean_absolute_error: 2.4173 Epoch 525/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.2018 - mean_absolute_error: 0.2286 - val_loss: 10.7894 - val_mean_absolute_error: 2.4448 Epoch 526/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1999 - mean_absolute_error: 0.2255 - val_loss: 11.0898 - val_mean_absolute_error: 2.4213 Epoch 527/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1873 - mean_absolute_error: 0.2198 - val_loss: 10.4409 - val_mean_absolute_error: 2.4020 Epoch 528/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1936 - mean_absolute_error: 0.2215 - val_loss: 10.6952 - val_mean_absolute_error: 2.4403 Epoch 529/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.2020 - mean_absolute_error: 0.2447 - val_loss: 11.5261 - val_mean_absolute_error: 2.4871 Epoch 530/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1992 - mean_absolute_error: 0.2316 - val_loss: 10.9164 - val_mean_absolute_error: 2.4746 Epoch 531/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1985 - mean_absolute_error: 0.2282 - val_loss: 10.7390 - val_mean_absolute_error: 2.4189 Epoch 532/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1990 - mean_absolute_error: 0.2455 - val_loss: 10.7981 - val_mean_absolute_error: 2.4432 Epoch 533/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1717 - mean_absolute_error: 0.2132 - val_loss: 10.8410 - val_mean_absolute_error: 2.4046 Epoch 534/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.1701 - mean_absolute_error: 0.2069 - val_loss: 10.4137 - val_mean_absolute_error: 2.3980 Epoch 535/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1640 - mean_absolute_error: 0.1989 - val_loss: 10.6653 - val_mean_absolute_error: 2.4326 Epoch 536/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1583 - mean_absolute_error: 0.1986 - val_loss: 10.8325 - val_mean_absolute_error: 2.4327 Epoch 537/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1640 - mean_absolute_error: 0.1925 - val_loss: 10.9230 - val_mean_absolute_error: 2.4260 Epoch 538/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1725 - mean_absolute_error: 0.2041 - val_loss: 10.9389 - val_mean_absolute_error: 2.4424 Epoch 539/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1624 - mean_absolute_error: 0.1960 - val_loss: 10.7745 - val_mean_absolute_error: 2.4465 Epoch 540/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1656 - mean_absolute_error: 0.1971 - val_loss: 10.7641 - val_mean_absolute_error: 2.4457 Epoch 541/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1540 - mean_absolute_error: 0.1867 - val_loss: 11.0202 - val_mean_absolute_error: 2.4479 Epoch 542/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1453 - mean_absolute_error: 0.1683 - val_loss: 10.7896 - val_mean_absolute_error: 2.4557 Epoch 543/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1627 - mean_absolute_error: 0.1911 - val_loss: 10.7662 - val_mean_absolute_error: 2.4474 Epoch 544/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1759 - mean_absolute_error: 0.2284 - val_loss: 10.9228 - val_mean_absolute_error: 2.4310 Epoch 545/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1936 - mean_absolute_error: 0.2393 - val_loss: 10.9231 - val_mean_absolute_error: 2.4637 Epoch 546/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2406 - mean_absolute_error: 0.2836 - val_loss: 10.9241 - val_mean_absolute_error: 2.4297 Epoch 547/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2491 - mean_absolute_error: 0.2936 - val_loss: 11.0189 - val_mean_absolute_error: 2.4921 Epoch 548/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2727 - mean_absolute_error: 0.2936 - val_loss: 10.3740 - val_mean_absolute_error: 2.3842 Epoch 549/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.2111 - mean_absolute_error: 0.2725 - val_loss: 10.5584 - val_mean_absolute_error: 2.3814 Epoch 550/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.2125 - mean_absolute_error: 0.2756 - val_loss: 11.0229 - val_mean_absolute_error: 2.4394 Epoch 551/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.1966 - mean_absolute_error: 0.2602 - val_loss: 10.5799 - val_mean_absolute_error: 2.4051 Epoch 552/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.2072 - mean_absolute_error: 0.2698 - val_loss: 11.1516 - val_mean_absolute_error: 2.4448 Epoch 553/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.2305 - mean_absolute_error: 0.3032 - val_loss: 10.3441 - val_mean_absolute_error: 2.3893 Epoch 554/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.2194 - mean_absolute_error: 0.2936 - val_loss: 11.1145 - val_mean_absolute_error: 2.4066 Epoch 555/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.1876 - mean_absolute_error: 0.2650 - val_loss: 10.8691 - val_mean_absolute_error: 2.3955 Epoch 556/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1924 - mean_absolute_error: 0.2629 - val_loss: 10.8592 - val_mean_absolute_error: 2.4251 Epoch 557/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.1789 - mean_absolute_error: 0.2385 - val_loss: 10.1336 - val_mean_absolute_error: 2.3885 Epoch 558/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1750 - mean_absolute_error: 0.2238 - val_loss: 11.1209 - val_mean_absolute_error: 2.4217 Epoch 559/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1529 - mean_absolute_error: 0.2097 - val_loss: 10.5811 - val_mean_absolute_error: 2.4095 Epoch 560/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1531 - mean_absolute_error: 0.1962 - val_loss: 10.8072 - val_mean_absolute_error: 2.4472 Epoch 561/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1573 - mean_absolute_error: 0.1978 - val_loss: 10.9593 - val_mean_absolute_error: 2.4025 Epoch 562/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1957 - mean_absolute_error: 0.2393 - val_loss: 10.8359 - val_mean_absolute_error: 2.4360 Epoch 563/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2017 - mean_absolute_error: 0.2641 - val_loss: 10.6207 - val_mean_absolute_error: 2.4344 Epoch 564/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2295 - mean_absolute_error: 0.3032 - val_loss: 10.6688 - val_mean_absolute_error: 2.3998 Epoch 565/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1961 - mean_absolute_error: 0.2814 - val_loss: 11.6109 - val_mean_absolute_error: 2.4728 Epoch 566/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1963 - mean_absolute_error: 0.2809 - val_loss: 10.4254 - val_mean_absolute_error: 2.3932 Epoch 567/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1677 - mean_absolute_error: 0.2523 - val_loss: 10.3427 - val_mean_absolute_error: 2.4071 Epoch 568/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1915 - mean_absolute_error: 0.2952 - val_loss: 10.5814 - val_mean_absolute_error: 2.3631 Epoch 569/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1996 - mean_absolute_error: 0.2868 - val_loss: 11.2250 - val_mean_absolute_error: 2.4102 Epoch 570/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1778 - mean_absolute_error: 0.2580 - val_loss: 10.5377 - val_mean_absolute_error: 2.3737 Epoch 571/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1772 - mean_absolute_error: 0.2348 - val_loss: 10.7093 - val_mean_absolute_error: 2.3965 Epoch 572/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.1737 - mean_absolute_error: 0.2352 - val_loss: 11.0808 - val_mean_absolute_error: 2.4205 Epoch 573/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1875 - mean_absolute_error: 0.2558 - val_loss: 10.6566 - val_mean_absolute_error: 2.4274 Epoch 574/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1663 - mean_absolute_error: 0.2357 - val_loss: 11.0931 - val_mean_absolute_error: 2.4194 Epoch 575/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1447 - mean_absolute_error: 0.2134 - val_loss: 10.7053 - val_mean_absolute_error: 2.3929 Epoch 576/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1531 - mean_absolute_error: 0.2130 - val_loss: 10.3977 - val_mean_absolute_error: 2.4253 Epoch 577/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1630 - mean_absolute_error: 0.2346 - val_loss: 11.2719 - val_mean_absolute_error: 2.4334 Epoch 578/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1819 - mean_absolute_error: 0.2603 - val_loss: 10.5289 - val_mean_absolute_error: 2.3532 Epoch 579/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1867 - mean_absolute_error: 0.2694 - val_loss: 10.1717 - val_mean_absolute_error: 2.4058 Epoch 580/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2370 - mean_absolute_error: 0.3178 - val_loss: 10.6675 - val_mean_absolute_error: 2.4338 Epoch 581/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1923 - mean_absolute_error: 0.2825 - val_loss: 11.1273 - val_mean_absolute_error: 2.3732 Epoch 582/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2191 - mean_absolute_error: 0.3179 - val_loss: 10.2221 - val_mean_absolute_error: 2.3042 Epoch 583/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2213 - mean_absolute_error: 0.3061 - val_loss: 10.4859 - val_mean_absolute_error: 2.4243 Epoch 584/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.1869 - mean_absolute_error: 0.2850 - val_loss: 11.6966 - val_mean_absolute_error: 2.4295 Epoch 585/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2168 - mean_absolute_error: 0.3091 - val_loss: 10.4318 - val_mean_absolute_error: 2.3474 Epoch 586/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1781 - mean_absolute_error: 0.2650 - val_loss: 10.5476 - val_mean_absolute_error: 2.4187 Epoch 587/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1597 - mean_absolute_error: 0.2584 - val_loss: 10.7476 - val_mean_absolute_error: 2.4137 Epoch 588/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.1465 - mean_absolute_error: 0.2341 - val_loss: 10.4324 - val_mean_absolute_error: 2.3849 Epoch 589/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1619 - mean_absolute_error: 0.2445 - val_loss: 10.3573 - val_mean_absolute_error: 2.3677 Epoch 590/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1667 - mean_absolute_error: 0.2596 - val_loss: 10.3388 - val_mean_absolute_error: 2.3814 Epoch 591/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2903 - mean_absolute_error: 0.3244 - val_loss: 10.9728 - val_mean_absolute_error: 2.4183 Epoch 592/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2065 - mean_absolute_error: 0.3152 - val_loss: 11.0100 - val_mean_absolute_error: 2.3455 Epoch 593/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2052 - mean_absolute_error: 0.3097 - val_loss: 10.5415 - val_mean_absolute_error: 2.3639 Epoch 594/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1628 - mean_absolute_error: 0.2530 - val_loss: 10.4131 - val_mean_absolute_error: 2.4191 Epoch 595/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2421 - mean_absolute_error: 0.2691 - val_loss: 11.6567 - val_mean_absolute_error: 2.4987 Epoch 596/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.2055 - mean_absolute_error: 0.2890 - val_loss: 10.4084 - val_mean_absolute_error: 2.4176 Epoch 597/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1310 - mean_absolute_error: 0.2635 - val_loss: 10.0647 - val_mean_absolute_error: 2.3447 Epoch 598/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1781 - mean_absolute_error: 0.2363 - val_loss: 10.8444 - val_mean_absolute_error: 2.4112 Epoch 599/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1549 - mean_absolute_error: 0.2526 - val_loss: 10.4082 - val_mean_absolute_error: 2.3908 Epoch 600/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2211 - mean_absolute_error: 0.2852 - val_loss: 10.9334 - val_mean_absolute_error: 2.4205 Epoch 601/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2272 - mean_absolute_error: 0.3043 - val_loss: 11.1079 - val_mean_absolute_error: 2.5064 Epoch 602/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1807 - mean_absolute_error: 0.2923 - val_loss: 10.6917 - val_mean_absolute_error: 2.3961 Epoch 603/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.2176 - mean_absolute_error: 0.3070 - val_loss: 9.9970 - val_mean_absolute_error: 2.3088 Epoch 604/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2949 - mean_absolute_error: 0.3834 - val_loss: 10.9545 - val_mean_absolute_error: 2.3861 Epoch 605/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.5294 - mean_absolute_error: 0.4159 - val_loss: 10.4197 - val_mean_absolute_error: 2.4002 Epoch 606/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.3624 - mean_absolute_error: 0.3747 - val_loss: 10.3236 - val_mean_absolute_error: 2.3832 Epoch 607/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3789 - mean_absolute_error: 0.4219 - val_loss: 10.5872 - val_mean_absolute_error: 2.3677 Epoch 608/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.2710 - mean_absolute_error: 0.3415 - val_loss: 10.4506 - val_mean_absolute_error: 2.3824 Epoch 609/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2370 - mean_absolute_error: 0.3277 - val_loss: 10.8797 - val_mean_absolute_error: 2.4055 Epoch 610/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.3320 - mean_absolute_error: 0.3776 - val_loss: 10.2735 - val_mean_absolute_error: 2.3267 Epoch 611/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3089 - mean_absolute_error: 0.3234 - val_loss: 10.6515 - val_mean_absolute_error: 2.4454 Epoch 612/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2457 - mean_absolute_error: 0.3061 - val_loss: 11.1509 - val_mean_absolute_error: 2.4476 Epoch 613/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2257 - mean_absolute_error: 0.3120 - val_loss: 11.0395 - val_mean_absolute_error: 2.3650 Epoch 614/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2132 - mean_absolute_error: 0.2952 - val_loss: 9.8566 - val_mean_absolute_error: 2.3550 Epoch 615/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1763 - mean_absolute_error: 0.2639 - val_loss: 11.1696 - val_mean_absolute_error: 2.4027 Epoch 616/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1864 - mean_absolute_error: 0.2506 - val_loss: 10.2579 - val_mean_absolute_error: 2.3726 Epoch 617/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1458 - mean_absolute_error: 0.2316 - val_loss: 10.6550 - val_mean_absolute_error: 2.3809 Epoch 618/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1794 - mean_absolute_error: 0.2606 - val_loss: 10.4313 - val_mean_absolute_error: 2.3773 Epoch 619/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1302 - mean_absolute_error: 0.2093 - val_loss: 10.7186 - val_mean_absolute_error: 2.3554 Epoch 620/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1709 - mean_absolute_error: 0.2624 - val_loss: 10.5483 - val_mean_absolute_error: 2.4136 Epoch 621/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2480 - mean_absolute_error: 0.2700 - val_loss: 10.9082 - val_mean_absolute_error: 2.4279 Epoch 622/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2125 - mean_absolute_error: 0.2656 - val_loss: 10.8669 - val_mean_absolute_error: 2.4106 Epoch 623/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2400 - mean_absolute_error: 0.3201 - val_loss: 10.2469 - val_mean_absolute_error: 2.3713 Epoch 624/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2208 - mean_absolute_error: 0.2993 - val_loss: 10.1343 - val_mean_absolute_error: 2.3921 Epoch 625/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1895 - mean_absolute_error: 0.2872 - val_loss: 10.8433 - val_mean_absolute_error: 2.3603 Epoch 626/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1594 - mean_absolute_error: 0.2610 - val_loss: 10.3551 - val_mean_absolute_error: 2.3417 Epoch 627/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1650 - mean_absolute_error: 0.2668 - val_loss: 10.7673 - val_mean_absolute_error: 2.4409 Epoch 628/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1567 - mean_absolute_error: 0.2648 - val_loss: 10.4859 - val_mean_absolute_error: 2.3958 Epoch 629/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1734 - mean_absolute_error: 0.2621 - val_loss: 10.2074 - val_mean_absolute_error: 2.3479 Epoch 630/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1597 - mean_absolute_error: 0.2549 - val_loss: 10.8053 - val_mean_absolute_error: 2.4274 Epoch 631/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1642 - mean_absolute_error: 0.2387 - val_loss: 10.1906 - val_mean_absolute_error: 2.3857 Epoch 632/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1940 - mean_absolute_error: 0.2424 - val_loss: 10.8726 - val_mean_absolute_error: 2.4334 Epoch 633/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1265 - mean_absolute_error: 0.2126 - val_loss: 10.5198 - val_mean_absolute_error: 2.3804 Epoch 634/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.1541 - mean_absolute_error: 0.2284 - val_loss: 10.2164 - val_mean_absolute_error: 2.3846 Epoch 635/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1558 - mean_absolute_error: 0.2229 - val_loss: 10.6983 - val_mean_absolute_error: 2.3936 Epoch 636/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.1231 - mean_absolute_error: 0.2026 - val_loss: 10.8690 - val_mean_absolute_error: 2.3920 Epoch 637/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1275 - mean_absolute_error: 0.2120 - val_loss: 10.3428 - val_mean_absolute_error: 2.3743 Epoch 638/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1141 - mean_absolute_error: 0.1981 - val_loss: 10.5270 - val_mean_absolute_error: 2.4340 Epoch 639/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1395 - mean_absolute_error: 0.2368 - val_loss: 10.7693 - val_mean_absolute_error: 2.4036 Epoch 640/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1651 - mean_absolute_error: 0.2408 - val_loss: 11.0505 - val_mean_absolute_error: 2.4186 Epoch 641/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1771 - mean_absolute_error: 0.2684 - val_loss: 10.7984 - val_mean_absolute_error: 2.4216 Epoch 642/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.4065 - mean_absolute_error: 0.3267 - val_loss: 10.0861 - val_mean_absolute_error: 2.3806 Epoch 643/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.6630 - mean_absolute_error: 0.5140 - val_loss: 11.2814 - val_mean_absolute_error: 2.4632 Epoch 644/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.8701 - mean_absolute_error: 0.6632 - val_loss: 9.7917 - val_mean_absolute_error: 2.2589 Epoch 645/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.0156 - mean_absolute_error: 0.6966 - val_loss: 10.1369 - val_mean_absolute_error: 2.3359 Epoch 646/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.8906 - mean_absolute_error: 0.6485 - val_loss: 11.6958 - val_mean_absolute_error: 2.5631 Epoch 647/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.6180 - mean_absolute_error: 0.5538 - val_loss: 10.6844 - val_mean_absolute_error: 2.4120 Epoch 648/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.5017 - mean_absolute_error: 0.4954 - val_loss: 10.3190 - val_mean_absolute_error: 2.3770 Epoch 649/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4223 - mean_absolute_error: 0.4578 - val_loss: 9.5874 - val_mean_absolute_error: 2.3471 Epoch 650/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3400 - mean_absolute_error: 0.4074 - val_loss: 9.2518 - val_mean_absolute_error: 2.3292 Epoch 651/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4383 - mean_absolute_error: 0.4640 - val_loss: 9.5912 - val_mean_absolute_error: 2.3872 Epoch 652/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4543 - mean_absolute_error: 0.4849 - val_loss: 10.7935 - val_mean_absolute_error: 2.4138 Epoch 653/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3728 - mean_absolute_error: 0.4489 - val_loss: 10.1092 - val_mean_absolute_error: 2.2988 Epoch 654/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3604 - mean_absolute_error: 0.4186 - val_loss: 9.7246 - val_mean_absolute_error: 2.3044 Epoch 655/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3067 - mean_absolute_error: 0.3879 - val_loss: 10.3488 - val_mean_absolute_error: 2.4429 Epoch 656/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2632 - mean_absolute_error: 0.3525 - val_loss: 11.1931 - val_mean_absolute_error: 2.4501 Epoch 657/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3304 - mean_absolute_error: 0.4068 - val_loss: 10.0513 - val_mean_absolute_error: 2.4106 Epoch 658/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2634 - mean_absolute_error: 0.3486 - val_loss: 10.3552 - val_mean_absolute_error: 2.3897 Epoch 659/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2111 - mean_absolute_error: 0.3181 - val_loss: 10.8019 - val_mean_absolute_error: 2.4729 Epoch 660/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1923 - mean_absolute_error: 0.2727 - val_loss: 10.6068 - val_mean_absolute_error: 2.4397 Epoch 661/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2085 - mean_absolute_error: 0.2779 - val_loss: 10.3626 - val_mean_absolute_error: 2.4328 Epoch 662/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1972 - mean_absolute_error: 0.2866 - val_loss: 9.9571 - val_mean_absolute_error: 2.3805 Epoch 663/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2015 - mean_absolute_error: 0.2986 - val_loss: 10.8264 - val_mean_absolute_error: 2.4147 Epoch 664/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2275 - mean_absolute_error: 0.3106 - val_loss: 10.3624 - val_mean_absolute_error: 2.3884 Epoch 665/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2606 - mean_absolute_error: 0.3369 - val_loss: 9.6471 - val_mean_absolute_error: 2.3481 Epoch 666/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2001 - mean_absolute_error: 0.3074 - val_loss: 10.5685 - val_mean_absolute_error: 2.4195 Epoch 667/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2097 - mean_absolute_error: 0.2968 - val_loss: 10.0843 - val_mean_absolute_error: 2.3795 Epoch 668/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1862 - mean_absolute_error: 0.2849 - val_loss: 9.8770 - val_mean_absolute_error: 2.3515 Epoch 669/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.1630 - mean_absolute_error: 0.2555 - val_loss: 10.7629 - val_mean_absolute_error: 2.4088 Epoch 670/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1396 - mean_absolute_error: 0.2246 - val_loss: 9.6465 - val_mean_absolute_error: 2.3781 Epoch 671/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.1541 - mean_absolute_error: 0.2522 - val_loss: 10.4173 - val_mean_absolute_error: 2.3964 Epoch 672/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1378 - mean_absolute_error: 0.2101 - val_loss: 10.4859 - val_mean_absolute_error: 2.4112 Epoch 673/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1259 - mean_absolute_error: 0.2103 - val_loss: 9.4698 - val_mean_absolute_error: 2.3189 Epoch 674/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1466 - mean_absolute_error: 0.2212 - val_loss: 10.2490 - val_mean_absolute_error: 2.3638 Epoch 675/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1357 - mean_absolute_error: 0.2169 - val_loss: 10.1685 - val_mean_absolute_error: 2.3847 Epoch 676/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.1263 - mean_absolute_error: 0.1880 - val_loss: 9.7661 - val_mean_absolute_error: 2.3544 Epoch 677/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1240 - mean_absolute_error: 0.2107 - val_loss: 10.6475 - val_mean_absolute_error: 2.3833 Epoch 678/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1245 - mean_absolute_error: 0.2098 - val_loss: 10.4232 - val_mean_absolute_error: 2.3969 Epoch 679/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1181 - mean_absolute_error: 0.1851 - val_loss: 9.8330 - val_mean_absolute_error: 2.3689 Epoch 680/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1000 - mean_absolute_error: 0.1617 - val_loss: 10.5502 - val_mean_absolute_error: 2.3856 Epoch 681/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1030 - mean_absolute_error: 0.1663 - val_loss: 10.0333 - val_mean_absolute_error: 2.3564 Epoch 682/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.1080 - mean_absolute_error: 0.1588 - val_loss: 10.0682 - val_mean_absolute_error: 2.3648 Epoch 683/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0954 - mean_absolute_error: 0.1496 - val_loss: 10.0174 - val_mean_absolute_error: 2.3649 Epoch 684/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0930 - mean_absolute_error: 0.1432 - val_loss: 10.2386 - val_mean_absolute_error: 2.3642 Epoch 685/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.0916 - mean_absolute_error: 0.1405 - val_loss: 10.0398 - val_mean_absolute_error: 2.3894 Epoch 686/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0995 - mean_absolute_error: 0.1569 - val_loss: 10.1684 - val_mean_absolute_error: 2.3801 Epoch 687/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0981 - mean_absolute_error: 0.1582 - val_loss: 10.1447 - val_mean_absolute_error: 2.3536 Epoch 688/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1108 - mean_absolute_error: 0.1573 - val_loss: 10.1180 - val_mean_absolute_error: 2.3867 Epoch 689/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.3149 - mean_absolute_error: 0.2487 - val_loss: 10.3141 - 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loss: 0.2268 - mean_absolute_error: 0.2616 - val_loss: 9.6834 - val_mean_absolute_error: 2.3762 Epoch 703/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.2670 - mean_absolute_error: 0.3151 - val_loss: 10.3504 - val_mean_absolute_error: 2.3488 Epoch 704/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.1960 - mean_absolute_error: 0.2540 - val_loss: 10.3162 - val_mean_absolute_error: 2.3315 Epoch 705/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.1811 - mean_absolute_error: 0.2568 - val_loss: 10.2779 - val_mean_absolute_error: 2.3721 Epoch 706/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1495 - mean_absolute_error: 0.2111 - val_loss: 10.0691 - val_mean_absolute_error: 2.4064 Epoch 707/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1670 - mean_absolute_error: 0.2372 - val_loss: 10.1739 - val_mean_absolute_error: 2.3739 Epoch 708/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1309 - mean_absolute_error: 0.1953 - val_loss: 10.5435 - val_mean_absolute_error: 2.3710 Epoch 709/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1382 - mean_absolute_error: 0.2115 - val_loss: 10.0229 - val_mean_absolute_error: 2.3694 Epoch 710/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1548 - mean_absolute_error: 0.2269 - val_loss: 10.0693 - val_mean_absolute_error: 2.3937 Epoch 711/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1407 - mean_absolute_error: 0.2196 - val_loss: 11.1673 - val_mean_absolute_error: 2.4491 Epoch 712/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1385 - mean_absolute_error: 0.2244 - val_loss: 10.1632 - val_mean_absolute_error: 2.3715 Epoch 713/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1600 - mean_absolute_error: 0.2175 - val_loss: 10.1589 - val_mean_absolute_error: 2.3583 Epoch 714/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1636 - mean_absolute_error: 0.1941 - val_loss: 10.5111 - val_mean_absolute_error: 2.3725 Epoch 715/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1673 - mean_absolute_error: 0.2592 - val_loss: 10.2272 - val_mean_absolute_error: 2.4079 Epoch 716/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2026 - mean_absolute_error: 0.2671 - val_loss: 11.0365 - val_mean_absolute_error: 2.4620 Epoch 717/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.1793 - mean_absolute_error: 0.2399 - val_loss: 10.1342 - val_mean_absolute_error: 2.3640 Epoch 718/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1429 - mean_absolute_error: 0.2109 - val_loss: 10.2653 - val_mean_absolute_error: 2.3329 Epoch 719/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1371 - mean_absolute_error: 0.2032 - val_loss: 10.1968 - val_mean_absolute_error: 2.3440 Epoch 720/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1251 - mean_absolute_error: 0.1832 - val_loss: 10.1238 - val_mean_absolute_error: 2.3591 Epoch 721/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1057 - mean_absolute_error: 0.1567 - val_loss: 10.3474 - val_mean_absolute_error: 2.3737 Epoch 722/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1068 - mean_absolute_error: 0.1594 - val_loss: 10.4411 - val_mean_absolute_error: 2.3631 Epoch 723/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1068 - mean_absolute_error: 0.1714 - val_loss: 10.3184 - val_mean_absolute_error: 2.3704 Epoch 724/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1304 - mean_absolute_error: 0.1971 - val_loss: 9.8914 - val_mean_absolute_error: 2.3762 Epoch 725/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2186 - mean_absolute_error: 0.2429 - val_loss: 10.3915 - val_mean_absolute_error: 2.3644 Epoch 726/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1849 - mean_absolute_error: 0.2211 - val_loss: 10.4496 - val_mean_absolute_error: 2.3643 Epoch 727/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1430 - mean_absolute_error: 0.2054 - val_loss: 10.0562 - val_mean_absolute_error: 2.3312 Epoch 728/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1271 - mean_absolute_error: 0.1913 - val_loss: 10.2529 - val_mean_absolute_error: 2.3881 Epoch 729/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.1202 - mean_absolute_error: 0.1610 - val_loss: 10.3018 - val_mean_absolute_error: 2.3849 Epoch 730/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1030 - mean_absolute_error: 0.1573 - val_loss: 10.1353 - val_mean_absolute_error: 2.3565 Epoch 731/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.1186 - mean_absolute_error: 0.1531 - val_loss: 10.2065 - val_mean_absolute_error: 2.3875 Epoch 732/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1111 - mean_absolute_error: 0.1482 - val_loss: 10.2270 - val_mean_absolute_error: 2.3761 Epoch 733/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1164 - mean_absolute_error: 0.1675 - val_loss: 10.0196 - val_mean_absolute_error: 2.3282 Epoch 734/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.1450 - mean_absolute_error: 0.1927 - val_loss: 10.2741 - val_mean_absolute_error: 2.3600 Epoch 735/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1167 - mean_absolute_error: 0.1967 - val_loss: 10.0381 - val_mean_absolute_error: 2.3566 Epoch 736/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1177 - mean_absolute_error: 0.1931 - val_loss: 10.1790 - val_mean_absolute_error: 2.3810 Epoch 737/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.1054 - mean_absolute_error: 0.1890 - val_loss: 10.3172 - val_mean_absolute_error: 2.3857 Epoch 738/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.0911 - mean_absolute_error: 0.1850 - val_loss: 10.2035 - val_mean_absolute_error: 2.3678 Epoch 739/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.0908 - mean_absolute_error: 0.1654 - val_loss: 10.1017 - val_mean_absolute_error: 2.3679 Epoch 740/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0783 - mean_absolute_error: 0.1567 - val_loss: 10.4459 - val_mean_absolute_error: 2.4033 Epoch 741/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0839 - mean_absolute_error: 0.1645 - val_loss: 10.5000 - val_mean_absolute_error: 2.3715 Epoch 742/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1059 - mean_absolute_error: 0.1807 - val_loss: 9.8059 - val_mean_absolute_error: 2.3921 Epoch 743/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0932 - mean_absolute_error: 0.1788 - val_loss: 10.0581 - val_mean_absolute_error: 2.3499 Epoch 744/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0944 - mean_absolute_error: 0.1767 - val_loss: 10.6674 - val_mean_absolute_error: 2.4088 Epoch 745/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1146 - mean_absolute_error: 0.1852 - val_loss: 10.1211 - val_mean_absolute_error: 2.3637 Epoch 746/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1269 - mean_absolute_error: 0.2060 - val_loss: 9.8502 - val_mean_absolute_error: 2.3593 Epoch 747/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1324 - mean_absolute_error: 0.2045 - val_loss: 10.7470 - val_mean_absolute_error: 2.4097 Epoch 748/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2082 - mean_absolute_error: 0.2335 - val_loss: 9.8711 - val_mean_absolute_error: 2.3177 Epoch 749/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1695 - mean_absolute_error: 0.2125 - val_loss: 10.3527 - val_mean_absolute_error: 2.3901 Epoch 750/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1402 - mean_absolute_error: 0.1774 - val_loss: 9.7843 - val_mean_absolute_error: 2.3359 Epoch 751/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1386 - mean_absolute_error: 0.1941 - val_loss: 10.1375 - val_mean_absolute_error: 2.3344 Epoch 752/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1037 - mean_absolute_error: 0.1642 - val_loss: 10.0714 - val_mean_absolute_error: 2.3372 Epoch 753/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0891 - mean_absolute_error: 0.1605 - val_loss: 10.0470 - val_mean_absolute_error: 2.3392 Epoch 754/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0893 - mean_absolute_error: 0.1552 - val_loss: 9.9866 - val_mean_absolute_error: 2.3521 Epoch 755/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2176 - mean_absolute_error: 0.2481 - val_loss: 10.4790 - val_mean_absolute_error: 2.3940 Epoch 756/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3476 - mean_absolute_error: 0.2930 - val_loss: 10.2879 - val_mean_absolute_error: 2.4231 Epoch 757/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2820 - mean_absolute_error: 0.3194 - val_loss: 9.9936 - val_mean_absolute_error: 2.3279 Epoch 758/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2459 - mean_absolute_error: 0.2971 - val_loss: 9.9874 - val_mean_absolute_error: 2.3077 Epoch 759/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1752 - mean_absolute_error: 0.2521 - val_loss: 10.1942 - val_mean_absolute_error: 2.3544 Epoch 760/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1574 - mean_absolute_error: 0.2332 - val_loss: 9.6953 - val_mean_absolute_error: 2.3219 Epoch 761/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1376 - mean_absolute_error: 0.1922 - val_loss: 9.9198 - val_mean_absolute_error: 2.3168 Epoch 762/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1222 - mean_absolute_error: 0.1818 - val_loss: 9.9291 - val_mean_absolute_error: 2.3207 Epoch 763/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1220 - mean_absolute_error: 0.1706 - val_loss: 10.1213 - val_mean_absolute_error: 2.3459 Epoch 764/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1481 - mean_absolute_error: 0.1754 - val_loss: 10.1768 - val_mean_absolute_error: 2.3286 Epoch 765/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1538 - mean_absolute_error: 0.2153 - val_loss: 9.7376 - val_mean_absolute_error: 2.3309 Epoch 766/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1675 - mean_absolute_error: 0.2572 - val_loss: 10.6375 - val_mean_absolute_error: 2.3984 Epoch 767/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1542 - mean_absolute_error: 0.2480 - val_loss: 10.2450 - val_mean_absolute_error: 2.3260 Epoch 768/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1767 - mean_absolute_error: 0.2785 - val_loss: 9.8988 - val_mean_absolute_error: 2.3135 Epoch 769/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1992 - mean_absolute_error: 0.2906 - val_loss: 10.0714 - val_mean_absolute_error: 2.3734 Epoch 770/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.3856 - mean_absolute_error: 0.3676 - val_loss: 10.2363 - val_mean_absolute_error: 2.3879 Epoch 771/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2177 - mean_absolute_error: 0.3136 - val_loss: 10.4639 - val_mean_absolute_error: 2.3518 Epoch 772/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2586 - mean_absolute_error: 0.3183 - val_loss: 9.8275 - val_mean_absolute_error: 2.3146 Epoch 773/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1822 - mean_absolute_error: 0.2783 - val_loss: 9.6529 - val_mean_absolute_error: 2.3076 Epoch 774/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.2057 - mean_absolute_error: 0.2944 - val_loss: 9.9646 - val_mean_absolute_error: 2.3496 Epoch 775/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1597 - mean_absolute_error: 0.2465 - val_loss: 10.4967 - val_mean_absolute_error: 2.3726 Epoch 776/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1544 - mean_absolute_error: 0.2298 - val_loss: 10.2042 - val_mean_absolute_error: 2.3717 Epoch 777/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1755 - mean_absolute_error: 0.2678 - val_loss: 10.0374 - val_mean_absolute_error: 2.3614 Epoch 778/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1431 - mean_absolute_error: 0.2366 - val_loss: 10.2364 - val_mean_absolute_error: 2.3751 Epoch 779/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1126 - mean_absolute_error: 0.1816 - val_loss: 10.0905 - val_mean_absolute_error: 2.3180 Epoch 780/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.1308 - mean_absolute_error: 0.2003 - val_loss: 9.7113 - val_mean_absolute_error: 2.3175 Epoch 781/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1075 - mean_absolute_error: 0.1850 - val_loss: 9.8982 - val_mean_absolute_error: 2.3344 Epoch 782/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1231 - mean_absolute_error: 0.2023 - val_loss: 10.0303 - val_mean_absolute_error: 2.3449 Epoch 783/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1640 - mean_absolute_error: 0.2351 - val_loss: 9.9323 - val_mean_absolute_error: 2.3505 Epoch 784/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1210 - mean_absolute_error: 0.1924 - val_loss: 10.2971 - val_mean_absolute_error: 2.3482 Epoch 785/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1157 - mean_absolute_error: 0.1966 - val_loss: 9.5108 - val_mean_absolute_error: 2.3066 Epoch 786/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1280 - mean_absolute_error: 0.2126 - val_loss: 10.2425 - val_mean_absolute_error: 2.3530 Epoch 787/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1564 - mean_absolute_error: 0.2345 - val_loss: 9.9248 - val_mean_absolute_error: 2.3703 Epoch 788/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1594 - mean_absolute_error: 0.2172 - val_loss: 10.0815 - val_mean_absolute_error: 2.3483 Epoch 789/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1717 - mean_absolute_error: 0.2609 - val_loss: 9.6467 - val_mean_absolute_error: 2.3185 Epoch 790/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1417 - mean_absolute_error: 0.2485 - val_loss: 10.0133 - val_mean_absolute_error: 2.3468 Epoch 791/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1494 - mean_absolute_error: 0.2410 - val_loss: 10.1566 - val_mean_absolute_error: 2.3588 Epoch 792/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1818 - mean_absolute_error: 0.2352 - val_loss: 9.9178 - val_mean_absolute_error: 2.3294 Epoch 793/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1675 - mean_absolute_error: 0.2244 - val_loss: 9.8559 - val_mean_absolute_error: 2.3569 Epoch 794/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2377 - mean_absolute_error: 0.2747 - val_loss: 9.9941 - val_mean_absolute_error: 2.3525 Epoch 795/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2328 - mean_absolute_error: 0.2644 - val_loss: 10.2322 - val_mean_absolute_error: 2.4078 Epoch 796/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1659 - mean_absolute_error: 0.2275 - val_loss: 9.9292 - val_mean_absolute_error: 2.3613 Epoch 797/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1353 - mean_absolute_error: 0.2140 - val_loss: 9.7356 - val_mean_absolute_error: 2.3040 Epoch 798/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1411 - mean_absolute_error: 0.2160 - val_loss: 9.3820 - val_mean_absolute_error: 2.2865 Epoch 799/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.1434 - mean_absolute_error: 0.2339 - val_loss: 10.0685 - val_mean_absolute_error: 2.3594 Epoch 800/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1796 - mean_absolute_error: 0.2853 - val_loss: 9.7468 - val_mean_absolute_error: 2.2655 Epoch 801/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1827 - mean_absolute_error: 0.2989 - val_loss: 9.8847 - val_mean_absolute_error: 2.3070 Epoch 802/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1518 - mean_absolute_error: 0.2527 - val_loss: 9.8165 - val_mean_absolute_error: 2.3520 Epoch 803/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1761 - mean_absolute_error: 0.2691 - val_loss: 10.6228 - val_mean_absolute_error: 2.3619 Epoch 804/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1706 - mean_absolute_error: 0.2865 - val_loss: 9.4497 - val_mean_absolute_error: 2.3268 Epoch 805/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1544 - mean_absolute_error: 0.2599 - val_loss: 10.1216 - val_mean_absolute_error: 2.3904 Epoch 806/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1306 - mean_absolute_error: 0.2277 - val_loss: 9.5613 - val_mean_absolute_error: 2.2801 Epoch 807/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1565 - mean_absolute_error: 0.2510 - val_loss: 9.6277 - val_mean_absolute_error: 2.2809 Epoch 808/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1260 - mean_absolute_error: 0.2273 - val_loss: 10.1417 - val_mean_absolute_error: 2.3427 Epoch 809/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1157 - mean_absolute_error: 0.2087 - val_loss: 10.2753 - val_mean_absolute_error: 2.3365 Epoch 810/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0962 - mean_absolute_error: 0.1809 - val_loss: 9.9123 - val_mean_absolute_error: 2.3645 Epoch 811/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1104 - mean_absolute_error: 0.1963 - val_loss: 9.5100 - val_mean_absolute_error: 2.3381 Epoch 812/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.1136 - mean_absolute_error: 0.2277 - val_loss: 10.0335 - val_mean_absolute_error: 2.3548 Epoch 813/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1042 - mean_absolute_error: 0.2081 - val_loss: 10.2245 - val_mean_absolute_error: 2.3471 Epoch 814/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1149 - mean_absolute_error: 0.2170 - val_loss: 10.0038 - val_mean_absolute_error: 2.3421 Epoch 815/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.1114 - mean_absolute_error: 0.2101 - val_loss: 9.9335 - val_mean_absolute_error: 2.3978 Epoch 816/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1072 - mean_absolute_error: 0.1900 - val_loss: 9.7490 - val_mean_absolute_error: 2.3276 Epoch 817/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1121 - mean_absolute_error: 0.1919 - val_loss: 9.8989 - val_mean_absolute_error: 2.3103 Epoch 818/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1305 - mean_absolute_error: 0.1966 - val_loss: 9.4392 - val_mean_absolute_error: 2.3313 Epoch 819/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1015 - mean_absolute_error: 0.2153 - val_loss: 9.7320 - val_mean_absolute_error: 2.3160 Epoch 820/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1119 - mean_absolute_error: 0.2018 - val_loss: 10.1481 - val_mean_absolute_error: 2.3213 Epoch 821/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1543 - mean_absolute_error: 0.2713 - val_loss: 9.8805 - val_mean_absolute_error: 2.3441 Epoch 822/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1490 - mean_absolute_error: 0.2520 - val_loss: 9.8380 - val_mean_absolute_error: 2.3566 Epoch 823/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1360 - mean_absolute_error: 0.2458 - val_loss: 10.0785 - val_mean_absolute_error: 2.3512 Epoch 824/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1714 - mean_absolute_error: 0.2554 - val_loss: 10.4272 - val_mean_absolute_error: 2.3674 Epoch 825/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1925 - mean_absolute_error: 0.2646 - val_loss: 9.6842 - val_mean_absolute_error: 2.3379 Epoch 826/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2804 - mean_absolute_error: 0.3053 - val_loss: 10.2486 - val_mean_absolute_error: 2.4644 Epoch 827/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2414 - mean_absolute_error: 0.3385 - val_loss: 10.6779 - val_mean_absolute_error: 2.3790 Epoch 828/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2292 - mean_absolute_error: 0.3373 - val_loss: 8.5862 - val_mean_absolute_error: 2.2413 Epoch 829/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.2418 - mean_absolute_error: 0.3153 - val_loss: 9.1988 - val_mean_absolute_error: 2.2596 Epoch 830/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3548 - mean_absolute_error: 0.3577 - val_loss: 10.0992 - val_mean_absolute_error: 2.3987 Epoch 831/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2333 - mean_absolute_error: 0.3435 - val_loss: 9.3266 - val_mean_absolute_error: 2.2870 Epoch 832/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2799 - mean_absolute_error: 0.3629 - val_loss: 8.8915 - val_mean_absolute_error: 2.2136 Epoch 833/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3545 - mean_absolute_error: 0.4474 - val_loss: 9.6896 - val_mean_absolute_error: 2.2284 Epoch 834/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.2580 - mean_absolute_error: 0.3870 - val_loss: 9.4169 - val_mean_absolute_error: 2.3404 Epoch 835/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.2539 - mean_absolute_error: 0.3706 - val_loss: 9.7283 - val_mean_absolute_error: 2.3361 Epoch 836/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.2156 - mean_absolute_error: 0.3128 - val_loss: 9.7134 - val_mean_absolute_error: 2.2836 Epoch 837/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.2464 - mean_absolute_error: 0.3475 - val_loss: 9.3315 - val_mean_absolute_error: 2.2975 Epoch 838/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.4040 - mean_absolute_error: 0.3930 - val_loss: 10.1315 - val_mean_absolute_error: 2.3800 Epoch 839/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.3585 - mean_absolute_error: 0.3707 - val_loss: 9.1701 - val_mean_absolute_error: 2.2536 Epoch 840/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.2135 - mean_absolute_error: 0.3105 - val_loss: 10.1400 - val_mean_absolute_error: 2.3480 Epoch 841/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.2204 - mean_absolute_error: 0.2947 - val_loss: 9.9063 - val_mean_absolute_error: 2.3540 Epoch 842/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.3301 - mean_absolute_error: 0.3574 - val_loss: 8.7144 - val_mean_absolute_error: 2.2200 Epoch 843/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.2444 - mean_absolute_error: 0.3541 - val_loss: 10.3508 - val_mean_absolute_error: 2.3409 Epoch 844/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2944 - mean_absolute_error: 0.4098 - val_loss: 9.7678 - val_mean_absolute_error: 2.3438 Epoch 845/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2759 - mean_absolute_error: 0.3997 - val_loss: 9.2358 - val_mean_absolute_error: 2.3273 Epoch 846/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2871 - mean_absolute_error: 0.3923 - val_loss: 9.8554 - val_mean_absolute_error: 2.3850 Epoch 847/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2234 - mean_absolute_error: 0.3646 - val_loss: 11.3354 - val_mean_absolute_error: 2.4403 Epoch 848/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2557 - mean_absolute_error: 0.3590 - val_loss: 9.3213 - val_mean_absolute_error: 2.3117 Epoch 849/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1465 - mean_absolute_error: 0.2803 - val_loss: 10.3101 - val_mean_absolute_error: 2.3573 Epoch 850/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1910 - mean_absolute_error: 0.2817 - val_loss: 9.9576 - val_mean_absolute_error: 2.3201 Epoch 851/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1502 - mean_absolute_error: 0.2887 - val_loss: 9.4299 - val_mean_absolute_error: 2.2701 Epoch 852/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1538 - mean_absolute_error: 0.2563 - val_loss: 9.3733 - val_mean_absolute_error: 2.3229 Epoch 853/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1391 - mean_absolute_error: 0.2442 - val_loss: 10.4008 - val_mean_absolute_error: 2.4038 Epoch 854/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3139 - mean_absolute_error: 0.2416 - val_loss: 9.4604 - val_mean_absolute_error: 2.3110 Epoch 855/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2701 - mean_absolute_error: 0.2492 - val_loss: 9.3546 - val_mean_absolute_error: 2.2727 Epoch 856/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1949 - mean_absolute_error: 0.2623 - val_loss: 10.3103 - val_mean_absolute_error: 2.3582 Epoch 857/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1651 - mean_absolute_error: 0.2506 - val_loss: 9.5093 - val_mean_absolute_error: 2.3721 Epoch 858/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1723 - mean_absolute_error: 0.2719 - val_loss: 10.1541 - val_mean_absolute_error: 2.3332 Epoch 859/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1501 - mean_absolute_error: 0.2499 - val_loss: 10.1119 - val_mean_absolute_error: 2.3665 Epoch 860/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1354 - mean_absolute_error: 0.2284 - val_loss: 9.6314 - val_mean_absolute_error: 2.3668 Epoch 861/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1342 - mean_absolute_error: 0.2424 - val_loss: 9.9307 - val_mean_absolute_error: 2.3339 Epoch 862/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1387 - mean_absolute_error: 0.2246 - val_loss: 10.0994 - val_mean_absolute_error: 2.3609 Epoch 863/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1182 - mean_absolute_error: 0.2163 - val_loss: 9.3258 - val_mean_absolute_error: 2.3695 Epoch 864/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1108 - mean_absolute_error: 0.2034 - val_loss: 10.0268 - val_mean_absolute_error: 2.3638 Epoch 865/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0789 - mean_absolute_error: 0.1788 - val_loss: 9.5128 - val_mean_absolute_error: 2.3130 Epoch 866/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0873 - mean_absolute_error: 0.1648 - val_loss: 9.6141 - val_mean_absolute_error: 2.3174 Epoch 867/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0690 - mean_absolute_error: 0.1504 - val_loss: 9.9993 - val_mean_absolute_error: 2.3406 Epoch 868/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0810 - mean_absolute_error: 0.1553 - val_loss: 9.8029 - val_mean_absolute_error: 2.3509 Epoch 869/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0708 - mean_absolute_error: 0.1487 - val_loss: 9.9710 - val_mean_absolute_error: 2.3374 Epoch 870/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0595 - mean_absolute_error: 0.1439 - val_loss: 9.6536 - val_mean_absolute_error: 2.3266 Epoch 871/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0636 - mean_absolute_error: 0.1438 - val_loss: 9.5346 - val_mean_absolute_error: 2.3164 Epoch 872/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0962 - mean_absolute_error: 0.1683 - val_loss: 10.0575 - val_mean_absolute_error: 2.3494 Epoch 873/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0813 - mean_absolute_error: 0.1859 - val_loss: 9.4436 - val_mean_absolute_error: 2.3205 Epoch 874/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0956 - mean_absolute_error: 0.1613 - val_loss: 9.6993 - val_mean_absolute_error: 2.3077 Epoch 875/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.4677 - mean_absolute_error: 0.3047 - val_loss: 9.7181 - val_mean_absolute_error: 2.3715 Epoch 876/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4813 - mean_absolute_error: 0.3411 - val_loss: 10.2577 - val_mean_absolute_error: 2.4307 Epoch 877/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3378 - mean_absolute_error: 0.3015 - val_loss: 9.7158 - val_mean_absolute_error: 2.2873 Epoch 878/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.2456 - mean_absolute_error: 0.2787 - val_loss: 9.6724 - val_mean_absolute_error: 2.2595 Epoch 879/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2001 - mean_absolute_error: 0.2526 - val_loss: 9.6855 - val_mean_absolute_error: 2.3293 Epoch 880/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.1872 - mean_absolute_error: 0.2416 - val_loss: 9.8330 - val_mean_absolute_error: 2.3284 Epoch 881/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.1501 - mean_absolute_error: 0.2066 - val_loss: 9.8352 - val_mean_absolute_error: 2.2861 Epoch 882/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1684 - mean_absolute_error: 0.2434 - val_loss: 9.5601 - val_mean_absolute_error: 2.3026 Epoch 883/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1359 - mean_absolute_error: 0.2115 - val_loss: 9.5208 - val_mean_absolute_error: 2.2859 Epoch 884/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1447 - mean_absolute_error: 0.1950 - val_loss: 9.6063 - val_mean_absolute_error: 2.2949 Epoch 885/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1250 - mean_absolute_error: 0.1846 - val_loss: 9.4987 - val_mean_absolute_error: 2.2974 Epoch 886/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1503 - mean_absolute_error: 0.1909 - val_loss: 9.9596 - val_mean_absolute_error: 2.3278 Epoch 887/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1098 - mean_absolute_error: 0.1732 - val_loss: 9.5967 - val_mean_absolute_error: 2.3080 Epoch 888/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1763 - mean_absolute_error: 0.1954 - val_loss: 9.8019 - val_mean_absolute_error: 2.2949 Epoch 889/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1607 - mean_absolute_error: 0.2124 - val_loss: 9.7368 - val_mean_absolute_error: 2.3342 Epoch 890/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1378 - mean_absolute_error: 0.2019 - val_loss: 9.9137 - val_mean_absolute_error: 2.3219 Epoch 891/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1505 - mean_absolute_error: 0.2193 - val_loss: 9.6684 - val_mean_absolute_error: 2.3083 Epoch 892/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1329 - mean_absolute_error: 0.1769 - val_loss: 9.5989 - val_mean_absolute_error: 2.3047 Epoch 893/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1562 - mean_absolute_error: 0.1952 - val_loss: 9.8837 - val_mean_absolute_error: 2.3443 Epoch 894/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1442 - mean_absolute_error: 0.2226 - val_loss: 9.7905 - val_mean_absolute_error: 2.3144 Epoch 895/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1664 - mean_absolute_error: 0.2747 - val_loss: 9.5331 - val_mean_absolute_error: 2.3190 Epoch 896/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.1580 - mean_absolute_error: 0.2662 - val_loss: 10.1353 - val_mean_absolute_error: 2.3363 Epoch 897/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.1324 - mean_absolute_error: 0.2448 - val_loss: 9.7743 - val_mean_absolute_error: 2.3231 Epoch 898/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1143 - mean_absolute_error: 0.2107 - val_loss: 9.6280 - val_mean_absolute_error: 2.3495 Epoch 899/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.0972 - mean_absolute_error: 0.1813 - val_loss: 10.0769 - val_mean_absolute_error: 2.3549 Epoch 900/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.0946 - mean_absolute_error: 0.1567 - val_loss: 9.7088 - val_mean_absolute_error: 2.3346 Epoch 901/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.2574 - mean_absolute_error: 0.2255 - val_loss: 9.3434 - val_mean_absolute_error: 2.2547 Epoch 902/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1657 - mean_absolute_error: 0.2624 - val_loss: 9.7276 - val_mean_absolute_error: 2.3440 Epoch 903/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1576 - mean_absolute_error: 0.2349 - val_loss: 10.2963 - val_mean_absolute_error: 2.3473 Epoch 904/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1352 - mean_absolute_error: 0.2278 - val_loss: 9.7690 - val_mean_absolute_error: 2.2699 Epoch 905/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1598 - mean_absolute_error: 0.2293 - val_loss: 9.0848 - val_mean_absolute_error: 2.2516 Epoch 906/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1477 - mean_absolute_error: 0.2585 - val_loss: 10.1780 - val_mean_absolute_error: 2.3617 Epoch 907/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1787 - mean_absolute_error: 0.2844 - val_loss: 9.9149 - val_mean_absolute_error: 2.3522 Epoch 908/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1790 - mean_absolute_error: 0.2759 - val_loss: 9.6591 - val_mean_absolute_error: 2.3326 Epoch 909/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1619 - mean_absolute_error: 0.2699 - val_loss: 10.2246 - val_mean_absolute_error: 2.3648 Epoch 910/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1535 - mean_absolute_error: 0.2477 - val_loss: 10.3153 - val_mean_absolute_error: 2.3867 Epoch 911/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1315 - mean_absolute_error: 0.2484 - val_loss: 9.5141 - val_mean_absolute_error: 2.3040 Epoch 912/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1155 - mean_absolute_error: 0.2250 - val_loss: 10.0245 - val_mean_absolute_error: 2.3195 Epoch 913/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1115 - mean_absolute_error: 0.1950 - val_loss: 9.9850 - val_mean_absolute_error: 2.3281 Epoch 914/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1930 - mean_absolute_error: 0.2165 - val_loss: 9.8310 - val_mean_absolute_error: 2.3283 Epoch 915/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2216 - mean_absolute_error: 0.2180 - val_loss: 10.2999 - val_mean_absolute_error: 2.3915 Epoch 916/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1203 - mean_absolute_error: 0.2283 - val_loss: 9.7901 - val_mean_absolute_error: 2.3422 Epoch 917/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1480 - mean_absolute_error: 0.2428 - val_loss: 9.8438 - val_mean_absolute_error: 2.3091 Epoch 918/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1283 - mean_absolute_error: 0.2280 - val_loss: 9.8470 - val_mean_absolute_error: 2.3292 Epoch 919/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2210 - mean_absolute_error: 0.2639 - val_loss: 9.9021 - val_mean_absolute_error: 2.3081 Epoch 920/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1515 - mean_absolute_error: 0.2295 - val_loss: 9.3645 - val_mean_absolute_error: 2.2721 Epoch 921/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1048 - mean_absolute_error: 0.1951 - val_loss: 10.1572 - val_mean_absolute_error: 2.3445 Epoch 922/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.0983 - mean_absolute_error: 0.1913 - val_loss: 9.7456 - val_mean_absolute_error: 2.3347 Epoch 923/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0924 - mean_absolute_error: 0.1704 - val_loss: 9.5136 - val_mean_absolute_error: 2.2934 Epoch 924/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0834 - mean_absolute_error: 0.1837 - val_loss: 10.1246 - val_mean_absolute_error: 2.3393 Epoch 925/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0879 - mean_absolute_error: 0.1809 - val_loss: 9.9775 - val_mean_absolute_error: 2.3647 Epoch 926/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0991 - mean_absolute_error: 0.1876 - val_loss: 9.7849 - val_mean_absolute_error: 2.3499 Epoch 927/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1398 - mean_absolute_error: 0.2128 - val_loss: 9.8738 - val_mean_absolute_error: 2.3446 Epoch 928/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1016 - mean_absolute_error: 0.2139 - val_loss: 9.6806 - val_mean_absolute_error: 2.3450 Epoch 929/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1025 - mean_absolute_error: 0.2164 - val_loss: 9.6901 - val_mean_absolute_error: 2.3454 Epoch 930/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0799 - mean_absolute_error: 0.1813 - val_loss: 10.2182 - val_mean_absolute_error: 2.3743 Epoch 931/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0768 - mean_absolute_error: 0.1816 - val_loss: 9.6026 - val_mean_absolute_error: 2.3305 Epoch 932/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.0883 - mean_absolute_error: 0.1591 - val_loss: 10.0524 - val_mean_absolute_error: 2.3673 Epoch 933/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.3199 - mean_absolute_error: 0.2548 - val_loss: 10.3891 - val_mean_absolute_error: 2.3891 Epoch 934/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2671 - mean_absolute_error: 0.2702 - val_loss: 9.1466 - val_mean_absolute_error: 2.2509 Epoch 935/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2032 - mean_absolute_error: 0.2722 - val_loss: 9.3928 - val_mean_absolute_error: 2.3009 Epoch 936/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1280 - mean_absolute_error: 0.2277 - val_loss: 10.1636 - val_mean_absolute_error: 2.3963 Epoch 937/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1196 - mean_absolute_error: 0.2216 - val_loss: 9.5919 - val_mean_absolute_error: 2.3296 Epoch 938/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1226 - mean_absolute_error: 0.2067 - val_loss: 9.8833 - val_mean_absolute_error: 2.3222 Epoch 939/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1103 - mean_absolute_error: 0.1967 - val_loss: 9.4949 - val_mean_absolute_error: 2.3214 Epoch 940/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0992 - mean_absolute_error: 0.1971 - val_loss: 9.6950 - val_mean_absolute_error: 2.3582 Epoch 941/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1016 - mean_absolute_error: 0.1958 - val_loss: 9.6778 - val_mean_absolute_error: 2.3505 Epoch 942/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0986 - mean_absolute_error: 0.1943 - val_loss: 9.7313 - val_mean_absolute_error: 2.3410 Epoch 943/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0724 - mean_absolute_error: 0.1639 - val_loss: 9.6356 - val_mean_absolute_error: 2.3386 Epoch 944/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0732 - mean_absolute_error: 0.1514 - val_loss: 9.7912 - val_mean_absolute_error: 2.3458 Epoch 945/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0855 - mean_absolute_error: 0.1535 - val_loss: 9.5431 - val_mean_absolute_error: 2.3308 Epoch 946/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0741 - mean_absolute_error: 0.1538 - val_loss: 9.6642 - val_mean_absolute_error: 2.3480 Epoch 947/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0939 - mean_absolute_error: 0.1620 - val_loss: 9.9339 - val_mean_absolute_error: 2.3484 Epoch 948/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0776 - mean_absolute_error: 0.1698 - val_loss: 9.7429 - val_mean_absolute_error: 2.3475 Epoch 949/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1244 - mean_absolute_error: 0.1952 - val_loss: 9.4335 - val_mean_absolute_error: 2.3311 Epoch 950/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2951 - mean_absolute_error: 0.2387 - val_loss: 9.6985 - val_mean_absolute_error: 2.3369 Epoch 951/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2142 - mean_absolute_error: 0.2488 - val_loss: 9.5136 - val_mean_absolute_error: 2.3188 Epoch 952/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1614 - mean_absolute_error: 0.2390 - val_loss: 9.5016 - val_mean_absolute_error: 2.3220 Epoch 953/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1372 - mean_absolute_error: 0.2155 - val_loss: 9.6219 - val_mean_absolute_error: 2.3919 Epoch 954/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.1003 - mean_absolute_error: 0.2038 - val_loss: 10.2632 - val_mean_absolute_error: 2.4191 Epoch 955/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.1378 - mean_absolute_error: 0.2202 - val_loss: 9.6009 - val_mean_absolute_error: 2.3480 Epoch 956/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0806 - mean_absolute_error: 0.1869 - val_loss: 9.3574 - val_mean_absolute_error: 2.3165 Epoch 957/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0828 - mean_absolute_error: 0.1715 - val_loss: 9.4472 - val_mean_absolute_error: 2.3095 Epoch 958/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0919 - mean_absolute_error: 0.1691 - val_loss: 9.5894 - val_mean_absolute_error: 2.3271 Epoch 959/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0576 - mean_absolute_error: 0.1505 - val_loss: 9.8180 - val_mean_absolute_error: 2.3589 Epoch 960/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0648 - mean_absolute_error: 0.1543 - val_loss: 9.6893 - val_mean_absolute_error: 2.3610 Epoch 961/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0369 - mean_absolute_error: 0.1231 - val_loss: 9.8386 - val_mean_absolute_error: 2.3627 Epoch 962/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.0475 - mean_absolute_error: 0.1234 - val_loss: 9.6366 - val_mean_absolute_error: 2.3278 Epoch 963/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0505 - mean_absolute_error: 0.1343 - val_loss: 9.9482 - val_mean_absolute_error: 2.3666 Epoch 964/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0523 - mean_absolute_error: 0.1354 - val_loss: 9.4746 - val_mean_absolute_error: 2.3289 Epoch 965/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0508 - mean_absolute_error: 0.1361 - val_loss: 9.7156 - val_mean_absolute_error: 2.3592 Epoch 966/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0624 - mean_absolute_error: 0.1583 - val_loss: 9.9157 - val_mean_absolute_error: 2.3661 Epoch 967/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1427 - mean_absolute_error: 0.1952 - val_loss: 10.1382 - val_mean_absolute_error: 2.3890 Epoch 968/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2321 - mean_absolute_error: 0.2482 - val_loss: 9.4973 - val_mean_absolute_error: 2.3290 Epoch 969/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.1582 - mean_absolute_error: 0.2618 - val_loss: 9.6214 - val_mean_absolute_error: 2.3476 Epoch 970/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3159 - mean_absolute_error: 0.3034 - val_loss: 9.8659 - val_mean_absolute_error: 2.4055 Epoch 971/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2633 - mean_absolute_error: 0.3015 - val_loss: 9.3563 - val_mean_absolute_error: 2.3549 Epoch 972/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2277 - mean_absolute_error: 0.2959 - val_loss: 9.6485 - val_mean_absolute_error: 2.3005 Epoch 973/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3695 - mean_absolute_error: 0.3722 - val_loss: 9.3264 - val_mean_absolute_error: 2.3886 Epoch 974/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3100 - mean_absolute_error: 0.3905 - val_loss: 9.9750 - val_mean_absolute_error: 2.3372 Epoch 975/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3895 - mean_absolute_error: 0.4153 - val_loss: 10.0259 - val_mean_absolute_error: 2.3074 Epoch 976/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3406 - mean_absolute_error: 0.4447 - val_loss: 9.7925 - val_mean_absolute_error: 2.3680 Epoch 977/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3946 - mean_absolute_error: 0.4559 - val_loss: 9.4000 - val_mean_absolute_error: 2.3237 Epoch 978/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.3513 - mean_absolute_error: 0.4578 - val_loss: 10.3918 - val_mean_absolute_error: 2.3141 Epoch 979/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.9213 - mean_absolute_error: 0.5757 - val_loss: 8.2951 - val_mean_absolute_error: 2.3032 Epoch 980/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.5378 - mean_absolute_error: 0.4763 - val_loss: 11.3056 - val_mean_absolute_error: 2.2972 Epoch 981/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.6109 - mean_absolute_error: 0.5284 - val_loss: 9.4833 - val_mean_absolute_error: 2.2990 Epoch 982/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.4959 - mean_absolute_error: 0.4951 - val_loss: 9.8858 - val_mean_absolute_error: 2.3354 Epoch 983/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.4903 - mean_absolute_error: 0.5183 - val_loss: 10.2157 - val_mean_absolute_error: 2.3516 Epoch 984/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.3702 - mean_absolute_error: 0.4340 - val_loss: 8.7655 - val_mean_absolute_error: 2.3304 Epoch 985/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.3843 - mean_absolute_error: 0.4272 - val_loss: 8.9265 - val_mean_absolute_error: 2.1943 Epoch 986/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.1769 - mean_absolute_error: 0.3130 - val_loss: 10.3095 - val_mean_absolute_error: 2.2915 Epoch 987/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.2672 - mean_absolute_error: 0.3518 - val_loss: 9.5768 - val_mean_absolute_error: 2.3120 Epoch 988/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1776 - mean_absolute_error: 0.3080 - val_loss: 9.2024 - val_mean_absolute_error: 2.2876 Epoch 989/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1483 - mean_absolute_error: 0.2574 - val_loss: 10.0151 - val_mean_absolute_error: 2.3455 Epoch 990/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0959 - mean_absolute_error: 0.2001 - val_loss: 9.6762 - val_mean_absolute_error: 2.3177 Epoch 991/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1134 - mean_absolute_error: 0.2136 - val_loss: 9.4056 - val_mean_absolute_error: 2.2867 Epoch 992/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0694 - mean_absolute_error: 0.1732 - val_loss: 9.4489 - val_mean_absolute_error: 2.2884 Epoch 993/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1096 - mean_absolute_error: 0.1918 - val_loss: 9.4864 - val_mean_absolute_error: 2.3156 Epoch 994/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0604 - mean_absolute_error: 0.1515 - val_loss: 9.8314 - val_mean_absolute_error: 2.3307 Epoch 995/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0597 - mean_absolute_error: 0.1650 - val_loss: 9.5169 - val_mean_absolute_error: 2.3071 Epoch 996/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0607 - mean_absolute_error: 0.1629 - val_loss: 9.3652 - val_mean_absolute_error: 2.2768 Epoch 997/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0533 - mean_absolute_error: 0.1438 - val_loss: 9.5636 - val_mean_absolute_error: 2.3117 Epoch 998/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0644 - mean_absolute_error: 0.1375 - val_loss: 9.7638 - val_mean_absolute_error: 2.3221 Epoch 999/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0675 - mean_absolute_error: 0.1321 - val_loss: 9.4684 - val_mean_absolute_error: 2.2953 Epoch 1000/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0612 - mean_absolute_error: 0.1446 - val_loss: 9.6163 - val_mean_absolute_error: 2.3081 7/7 [==============================] - 0s 6ms/step 1/1 [==============================] - 0s 20ms/step 1/1 [==============================] - 0s 22ms/step
# Calculate the RMSE
rmse_lstm = sqrt(mean_squared_error(y_test_BMW, test_predictions_BMW))
print('The RMSE value of LSTM model (BMW): {:.4f}'.format(rmse_lstm))
The RMSE value of LSTM model (BMW): 2.5942
# Plot Training Observations VS Training Predictions
figure(figsize=(20, 10), dpi=100)
plt.plot(dates_train_BMW, train_predictions_BMW)
plt.plot(dates_train_BMW, y_train_BMW)
plt.title('LSTM: Training Actual Returns/Training Predicted Returns (BMW)', fontsize=16)
plt.legend(['BMW Training Predictions', 'BMW Training Observations'])
# Plot Testing Observations VS Testing Predictions
figure(figsize=(20, 10), dpi=100)
plt.plot(dates_test_BMW, test_predictions_BMW)
plt.plot(dates_test_BMW, y_test_BMW)
plt.title('LSTM: Testing Actual Returns/Testing Predicted Returns (BMW)', fontsize=16)
plt.legend(['BMW Testing Predictions', 'BMW Testing Observations'])
# General Plot (Training, Validation & testing)
figure(figsize=(20, 10), dpi=100)
plt.plot(dates_train_BMW, train_predictions_BMW)
plt.plot(dates_train_BMW, y_train_BMW)
plt.plot(dates_val_BMW, val_predictions_BMW)
plt.plot(dates_val_BMW, y_val_BMW)
plt.plot(dates_test_BMW, test_predictions_BMW)
plt.plot(dates_test_BMW, y_test_BMW)
plt.title('LSTM: General forecasting plot (BMW)', fontsize=16)
plt.legend(['BMW Training Predictions',
'BMW Training Observations',
'BMW Validation Predictions',
'BMW Validation Observations',
'BMW Testing Predictions',
'BMW Testing Observations'])
<matplotlib.legend.Legend at 0x7fa8c31e54f0>
# transform a time series dataset into a supervised learning dataset (Input : Output)
def F_to_windowed_F(F_RET, first_date_str, last_date_str, n=3):
first_date = str_to_datetime(first_date_str)
last_date = str_to_datetime(last_date_str)
target_date = first_date
dates_F = []
X, Y = [], []
last_time = False
while True:
F_subset = F_RET.loc[:target_date].tail(n+1)
if len(F_subset) != n+1:
print(f'Error: Window of size {n} is too large for date {target_date}')
return
values = F_subset['Ret_F'].to_numpy()
x, y = values[:-1], values[-1]
dates_F.append(target_date)
X.append(x)
Y.append(y)
next_week = F_RET.loc[target_date:target_date+datetime.timedelta(days=7)]
next_datetime_str = str(next_week.head(2).tail(1).index.values[0])
next_date_str = next_datetime_str.split('T')[0]
year_month_day = next_date_str.split('-')
year, month, day = year_month_day
next_date = datetime.datetime(day=int(day), month=int(month), year=int(year))
if last_time:
break
target_date = next_date
if target_date == last_date:
last_time = True
ret_F = pd.DataFrame({})
ret_F['Target Date'] = dates_F
X = np.array(X)
for i in range(0, n):
X[:, i]
ret_F[f'Target-{n-i}'] = X[:, i]
ret_F['Target'] = Y
return ret_F
# Start day second time around: '2020-01-03'
windowed_F = F_to_windowed_F(F_RET,
'2020-01-03',
'2020-12-30',
n=3)
# Convert our new dataset into numpy arrays (to feed it directly into a tensorflow model)
def windowed_F_to_date_X_y(windowed_dataframe):
F_as_np = windowed_dataframe.to_numpy()
dates_F = F_as_np[:, 0]
middle_matrix_F = F_as_np[:, 1:-1]
X_F = middle_matrix_F.reshape((len(dates_F), middle_matrix_F.shape[1], 1))
Y_F = F_as_np[:, -1]
return dates_F, X_F.astype(np.float32), Y_F.astype(np.float32)
dates_F, X_F, y_F = windowed_F_to_date_X_y(windowed_F)
# Split the data into training, validation and testing partitions
q_85_F = int(len(dates_F) * .85)
q_95_F = int(len(dates_F) * .95)
dates_train_F, X_train_F, y_train_F = dates_F[:q_85_F], X_F[:q_85_F], y_F[:q_85_F]
dates_val_F, X_val_F, y_val_F = dates_F[q_85_F:q_95_F], X_F[q_85_F:q_95_F], y_F[q_85_F:q_95_F]
dates_test_F, X_test_F, y_test_F = dates_F[q_95_F:], X_F[q_95_F:], y_F[q_95_F:]
# Create & train the LSTM model
model_F = Sequential([layers.Input((3, 1)),
layers.LSTM(264),
layers.Dense(132, activation='relu'),
layers.Dense(132, activation='relu'),
layers.Dense(1)])
model_F.compile(loss='mse',
optimizer=Adam(learning_rate=0.001),
metrics=['mean_absolute_error'])
# Fitting the LSTM model
model_F.fit(X_train_F, y_train_F, validation_data=(X_val_F, y_val_F), epochs=1000)
# Forecasting
train_predictions_F = model_F.predict(X_train_F).flatten()
val_predictions_F = model_F.predict(X_val_F).flatten()
test_predictions_F = model_F.predict(X_test_F).flatten()
Epoch 1/1000 7/7 [==============================] - 3s 124ms/step - loss: 14.1291 - mean_absolute_error: 2.5834 - val_loss: 7.0807 - val_mean_absolute_error: 2.0285 Epoch 2/1000 7/7 [==============================] - 0s 26ms/step - loss: 13.9391 - mean_absolute_error: 2.5655 - val_loss: 7.0867 - val_mean_absolute_error: 2.0259 Epoch 3/1000 7/7 [==============================] - 0s 24ms/step - loss: 13.8637 - mean_absolute_error: 2.5562 - val_loss: 7.1496 - val_mean_absolute_error: 2.0390 Epoch 4/1000 7/7 [==============================] - 0s 20ms/step - loss: 13.7357 - mean_absolute_error: 2.5421 - val_loss: 7.3126 - val_mean_absolute_error: 2.0601 Epoch 5/1000 7/7 [==============================] - 0s 27ms/step - loss: 13.5073 - mean_absolute_error: 2.5340 - val_loss: 7.3706 - val_mean_absolute_error: 2.0729 Epoch 6/1000 7/7 [==============================] - 0s 24ms/step - loss: 13.2535 - mean_absolute_error: 2.5173 - val_loss: 7.5392 - val_mean_absolute_error: 2.1168 Epoch 7/1000 7/7 [==============================] - 0s 21ms/step - loss: 13.0932 - mean_absolute_error: 2.5187 - val_loss: 7.8834 - val_mean_absolute_error: 2.1596 Epoch 8/1000 7/7 [==============================] - 0s 21ms/step - loss: 12.8905 - mean_absolute_error: 2.4917 - val_loss: 7.9014 - val_mean_absolute_error: 2.1838 Epoch 9/1000 7/7 [==============================] - 0s 24ms/step - loss: 12.5447 - mean_absolute_error: 2.4584 - val_loss: 8.2498 - val_mean_absolute_error: 2.1904 Epoch 10/1000 7/7 [==============================] - 0s 29ms/step - loss: 12.3602 - mean_absolute_error: 2.4688 - val_loss: 8.5228 - val_mean_absolute_error: 2.2142 Epoch 11/1000 7/7 [==============================] - 0s 21ms/step - loss: 12.0978 - mean_absolute_error: 2.4279 - val_loss: 8.5425 - val_mean_absolute_error: 2.2175 Epoch 12/1000 7/7 [==============================] - 0s 22ms/step - loss: 12.2890 - mean_absolute_error: 2.4367 - val_loss: 8.7267 - val_mean_absolute_error: 2.2576 Epoch 13/1000 7/7 [==============================] - 0s 24ms/step - loss: 12.0918 - mean_absolute_error: 2.4092 - val_loss: 9.0535 - val_mean_absolute_error: 2.3111 Epoch 14/1000 7/7 [==============================] - 0s 22ms/step - loss: 12.0066 - mean_absolute_error: 2.4312 - val_loss: 9.1410 - val_mean_absolute_error: 2.3255 Epoch 15/1000 7/7 [==============================] - 0s 23ms/step - loss: 12.2807 - mean_absolute_error: 2.4416 - val_loss: 8.9239 - val_mean_absolute_error: 2.3264 Epoch 16/1000 7/7 [==============================] - 0s 23ms/step - loss: 11.8921 - mean_absolute_error: 2.4094 - val_loss: 9.4019 - val_mean_absolute_error: 2.3824 Epoch 17/1000 7/7 [==============================] - 0s 23ms/step - loss: 11.7708 - mean_absolute_error: 2.4086 - val_loss: 9.2441 - val_mean_absolute_error: 2.3567 Epoch 18/1000 7/7 [==============================] - 0s 20ms/step - loss: 11.6456 - mean_absolute_error: 2.3842 - val_loss: 9.1971 - val_mean_absolute_error: 2.3654 Epoch 19/1000 7/7 [==============================] - 0s 26ms/step - loss: 11.5662 - mean_absolute_error: 2.3678 - val_loss: 9.2678 - val_mean_absolute_error: 2.3590 Epoch 20/1000 7/7 [==============================] - 0s 25ms/step - loss: 11.4833 - mean_absolute_error: 2.3602 - val_loss: 9.4341 - val_mean_absolute_error: 2.3867 Epoch 21/1000 7/7 [==============================] - 0s 23ms/step - loss: 11.4236 - mean_absolute_error: 2.3533 - val_loss: 10.1684 - val_mean_absolute_error: 2.4899 Epoch 22/1000 7/7 [==============================] - 0s 29ms/step - loss: 11.4527 - mean_absolute_error: 2.3592 - val_loss: 9.9754 - val_mean_absolute_error: 2.4418 Epoch 23/1000 7/7 [==============================] - 0s 22ms/step - loss: 11.2770 - mean_absolute_error: 2.3348 - val_loss: 9.9431 - val_mean_absolute_error: 2.4530 Epoch 24/1000 7/7 [==============================] - 0s 21ms/step - loss: 11.3230 - mean_absolute_error: 2.3392 - val_loss: 9.7826 - val_mean_absolute_error: 2.4282 Epoch 25/1000 7/7 [==============================] - 0s 21ms/step - loss: 11.1675 - mean_absolute_error: 2.3106 - val_loss: 9.4996 - val_mean_absolute_error: 2.3806 Epoch 26/1000 7/7 [==============================] - 0s 23ms/step - loss: 11.1402 - mean_absolute_error: 2.3093 - val_loss: 9.8982 - val_mean_absolute_error: 2.4337 Epoch 27/1000 7/7 [==============================] - 0s 24ms/step - loss: 11.0842 - mean_absolute_error: 2.2957 - val_loss: 10.0753 - val_mean_absolute_error: 2.4645 Epoch 28/1000 7/7 [==============================] - 0s 27ms/step - loss: 11.0275 - mean_absolute_error: 2.2754 - val_loss: 9.9566 - val_mean_absolute_error: 2.4434 Epoch 29/1000 7/7 [==============================] - 0s 27ms/step - loss: 10.9377 - mean_absolute_error: 2.2730 - val_loss: 9.6514 - val_mean_absolute_error: 2.4057 Epoch 30/1000 7/7 [==============================] - 0s 23ms/step - loss: 11.2689 - mean_absolute_error: 2.3121 - val_loss: 9.7499 - val_mean_absolute_error: 2.3991 Epoch 31/1000 7/7 [==============================] - 0s 23ms/step - loss: 10.9052 - mean_absolute_error: 2.2973 - val_loss: 11.2479 - val_mean_absolute_error: 2.5785 Epoch 32/1000 7/7 [==============================] - 0s 25ms/step - loss: 11.1196 - mean_absolute_error: 2.3347 - val_loss: 10.4624 - val_mean_absolute_error: 2.5108 Epoch 33/1000 7/7 [==============================] - 0s 23ms/step - loss: 10.9622 - mean_absolute_error: 2.2944 - val_loss: 9.9672 - val_mean_absolute_error: 2.4432 Epoch 34/1000 7/7 [==============================] - 0s 29ms/step - loss: 10.8660 - mean_absolute_error: 2.2960 - val_loss: 9.8879 - val_mean_absolute_error: 2.3868 Epoch 35/1000 7/7 [==============================] - 0s 25ms/step - loss: 10.8594 - mean_absolute_error: 2.3280 - val_loss: 9.8612 - val_mean_absolute_error: 2.3811 Epoch 36/1000 7/7 [==============================] - 0s 38ms/step - loss: 10.3712 - mean_absolute_error: 2.2288 - val_loss: 9.5292 - val_mean_absolute_error: 2.3426 Epoch 37/1000 7/7 [==============================] - 0s 21ms/step - loss: 10.4636 - mean_absolute_error: 2.2212 - val_loss: 9.8332 - val_mean_absolute_error: 2.4108 Epoch 38/1000 7/7 [==============================] - 0s 19ms/step - loss: 10.2697 - mean_absolute_error: 2.2034 - val_loss: 10.2315 - val_mean_absolute_error: 2.4906 Epoch 39/1000 7/7 [==============================] - 0s 20ms/step - loss: 10.1839 - mean_absolute_error: 2.1847 - val_loss: 10.4087 - val_mean_absolute_error: 2.4769 Epoch 40/1000 7/7 [==============================] - 0s 19ms/step - loss: 10.0477 - mean_absolute_error: 2.1751 - val_loss: 9.6564 - val_mean_absolute_error: 2.3301 Epoch 41/1000 7/7 [==============================] - 0s 22ms/step - loss: 10.0771 - mean_absolute_error: 2.1842 - val_loss: 9.2985 - val_mean_absolute_error: 2.3024 Epoch 42/1000 7/7 [==============================] - 0s 20ms/step - loss: 9.8644 - mean_absolute_error: 2.1624 - val_loss: 9.7696 - val_mean_absolute_error: 2.3851 Epoch 43/1000 7/7 [==============================] - 0s 23ms/step - loss: 9.8827 - mean_absolute_error: 2.1357 - val_loss: 10.2205 - val_mean_absolute_error: 2.4393 Epoch 44/1000 7/7 [==============================] - 0s 23ms/step - loss: 9.7565 - mean_absolute_error: 2.1430 - val_loss: 10.5909 - val_mean_absolute_error: 2.4802 Epoch 45/1000 7/7 [==============================] - 0s 21ms/step - loss: 9.5599 - mean_absolute_error: 2.1212 - val_loss: 10.0780 - val_mean_absolute_error: 2.4159 Epoch 46/1000 7/7 [==============================] - 0s 21ms/step - loss: 9.4101 - mean_absolute_error: 2.0912 - val_loss: 9.8604 - val_mean_absolute_error: 2.3731 Epoch 47/1000 7/7 [==============================] - 0s 19ms/step - loss: 9.3061 - mean_absolute_error: 2.0897 - val_loss: 9.6991 - val_mean_absolute_error: 2.3411 Epoch 48/1000 7/7 [==============================] - 0s 21ms/step - loss: 9.1731 - mean_absolute_error: 2.0701 - val_loss: 9.9547 - val_mean_absolute_error: 2.3612 Epoch 49/1000 7/7 [==============================] - 0s 25ms/step - loss: 9.6322 - mean_absolute_error: 2.1471 - val_loss: 9.4072 - val_mean_absolute_error: 2.2369 Epoch 50/1000 7/7 [==============================] - 0s 21ms/step - loss: 9.4552 - mean_absolute_error: 2.1394 - val_loss: 9.8122 - val_mean_absolute_error: 2.3697 Epoch 51/1000 7/7 [==============================] - 0s 20ms/step - loss: 9.4339 - mean_absolute_error: 2.1118 - val_loss: 9.1650 - val_mean_absolute_error: 2.2030 Epoch 52/1000 7/7 [==============================] - 0s 19ms/step - loss: 9.0728 - mean_absolute_error: 2.1237 - val_loss: 10.5546 - val_mean_absolute_error: 2.3795 Epoch 53/1000 7/7 [==============================] - 0s 21ms/step - loss: 9.6263 - mean_absolute_error: 2.1508 - val_loss: 11.5147 - val_mean_absolute_error: 2.5186 Epoch 54/1000 7/7 [==============================] - 0s 22ms/step - loss: 8.7771 - mean_absolute_error: 2.0550 - val_loss: 9.4583 - val_mean_absolute_error: 2.2238 Epoch 55/1000 7/7 [==============================] - 0s 22ms/step - loss: 8.9534 - mean_absolute_error: 2.1004 - val_loss: 10.0321 - val_mean_absolute_error: 2.3026 Epoch 56/1000 7/7 [==============================] - 0s 19ms/step - loss: 8.5854 - mean_absolute_error: 2.0314 - val_loss: 11.1471 - val_mean_absolute_error: 2.4122 Epoch 57/1000 7/7 [==============================] - 0s 18ms/step - loss: 8.3890 - mean_absolute_error: 2.0036 - val_loss: 11.4214 - val_mean_absolute_error: 2.4737 Epoch 58/1000 7/7 [==============================] - 0s 21ms/step - loss: 8.2104 - mean_absolute_error: 2.0119 - val_loss: 10.7860 - val_mean_absolute_error: 2.4429 Epoch 59/1000 7/7 [==============================] - 0s 21ms/step - loss: 8.6702 - mean_absolute_error: 2.0398 - val_loss: 10.1954 - val_mean_absolute_error: 2.3537 Epoch 60/1000 7/7 [==============================] - 0s 21ms/step - loss: 8.3662 - mean_absolute_error: 2.0314 - val_loss: 10.6478 - val_mean_absolute_error: 2.3401 Epoch 61/1000 7/7 [==============================] - 0s 19ms/step - loss: 8.1682 - mean_absolute_error: 2.0293 - val_loss: 11.3555 - val_mean_absolute_error: 2.4043 Epoch 62/1000 7/7 [==============================] - 0s 18ms/step - loss: 7.7714 - mean_absolute_error: 1.9552 - val_loss: 12.3390 - val_mean_absolute_error: 2.6163 Epoch 63/1000 7/7 [==============================] - 0s 29ms/step - loss: 7.6841 - mean_absolute_error: 1.9462 - val_loss: 11.5072 - val_mean_absolute_error: 2.5035 Epoch 64/1000 7/7 [==============================] - 0s 20ms/step - loss: 7.4644 - mean_absolute_error: 1.9046 - val_loss: 12.8641 - val_mean_absolute_error: 2.6359 Epoch 65/1000 7/7 [==============================] - 0s 20ms/step - loss: 7.4082 - mean_absolute_error: 1.9244 - val_loss: 12.0557 - val_mean_absolute_error: 2.5560 Epoch 66/1000 7/7 [==============================] - 0s 18ms/step - loss: 7.2827 - mean_absolute_error: 1.8911 - val_loss: 11.7973 - val_mean_absolute_error: 2.4734 Epoch 67/1000 7/7 [==============================] - 0s 18ms/step - loss: 7.0079 - mean_absolute_error: 1.8802 - val_loss: 11.9206 - val_mean_absolute_error: 2.4847 Epoch 68/1000 7/7 [==============================] - 0s 19ms/step - loss: 7.0091 - mean_absolute_error: 1.8499 - val_loss: 13.1290 - val_mean_absolute_error: 2.6753 Epoch 69/1000 7/7 [==============================] - 0s 18ms/step - loss: 6.8455 - mean_absolute_error: 1.8620 - val_loss: 12.4971 - val_mean_absolute_error: 2.5650 Epoch 70/1000 7/7 [==============================] - 0s 29ms/step - loss: 6.6914 - mean_absolute_error: 1.8285 - val_loss: 12.4712 - val_mean_absolute_error: 2.6004 Epoch 71/1000 7/7 [==============================] - 0s 22ms/step - loss: 6.9221 - mean_absolute_error: 1.8876 - val_loss: 12.9546 - val_mean_absolute_error: 2.5729 Epoch 72/1000 7/7 [==============================] - 0s 25ms/step - loss: 7.0243 - mean_absolute_error: 1.9262 - val_loss: 13.4260 - val_mean_absolute_error: 2.6940 Epoch 73/1000 7/7 [==============================] - 0s 20ms/step - loss: 7.5388 - mean_absolute_error: 1.9851 - val_loss: 12.5164 - val_mean_absolute_error: 2.5778 Epoch 74/1000 7/7 [==============================] - 0s 21ms/step - loss: 6.6167 - mean_absolute_error: 1.8804 - val_loss: 11.6695 - val_mean_absolute_error: 2.4646 Epoch 75/1000 7/7 [==============================] - 0s 24ms/step - loss: 6.4589 - mean_absolute_error: 1.8717 - val_loss: 13.0322 - val_mean_absolute_error: 2.6288 Epoch 76/1000 7/7 [==============================] - 0s 20ms/step - loss: 6.4966 - mean_absolute_error: 1.8898 - val_loss: 12.5415 - val_mean_absolute_error: 2.5807 Epoch 77/1000 7/7 [==============================] - 0s 19ms/step - loss: 6.3987 - mean_absolute_error: 1.8664 - val_loss: 13.0951 - val_mean_absolute_error: 2.6581 Epoch 78/1000 7/7 [==============================] - 0s 19ms/step - loss: 6.7639 - mean_absolute_error: 1.8968 - val_loss: 12.9709 - val_mean_absolute_error: 2.6692 Epoch 79/1000 7/7 [==============================] - 0s 20ms/step - loss: 5.9949 - mean_absolute_error: 1.8029 - val_loss: 12.0733 - val_mean_absolute_error: 2.5223 Epoch 80/1000 7/7 [==============================] - 0s 20ms/step - loss: 6.3129 - mean_absolute_error: 1.8373 - val_loss: 13.8091 - val_mean_absolute_error: 2.7022 Epoch 81/1000 7/7 [==============================] - 0s 18ms/step - loss: 5.9129 - mean_absolute_error: 1.8043 - val_loss: 12.6100 - val_mean_absolute_error: 2.6540 Epoch 82/1000 7/7 [==============================] - 0s 21ms/step - loss: 5.6354 - mean_absolute_error: 1.7401 - val_loss: 14.1394 - val_mean_absolute_error: 2.8373 Epoch 83/1000 7/7 [==============================] - 0s 22ms/step - loss: 5.7268 - mean_absolute_error: 1.7499 - val_loss: 12.1423 - val_mean_absolute_error: 2.4822 Epoch 84/1000 7/7 [==============================] - 0s 22ms/step - loss: 5.5299 - mean_absolute_error: 1.7314 - val_loss: 12.9598 - val_mean_absolute_error: 2.6353 Epoch 85/1000 7/7 [==============================] - 0s 30ms/step - loss: 5.2637 - mean_absolute_error: 1.7017 - val_loss: 13.3887 - val_mean_absolute_error: 2.6412 Epoch 86/1000 7/7 [==============================] - 0s 36ms/step - loss: 5.3491 - mean_absolute_error: 1.7002 - val_loss: 12.8123 - val_mean_absolute_error: 2.5599 Epoch 87/1000 7/7 [==============================] - 0s 21ms/step - loss: 5.1733 - mean_absolute_error: 1.6542 - val_loss: 13.6525 - val_mean_absolute_error: 2.7312 Epoch 88/1000 7/7 [==============================] - 0s 21ms/step - loss: 4.9805 - mean_absolute_error: 1.6382 - val_loss: 12.8467 - val_mean_absolute_error: 2.5891 Epoch 89/1000 7/7 [==============================] - 0s 22ms/step - loss: 5.0993 - mean_absolute_error: 1.6193 - val_loss: 12.6817 - val_mean_absolute_error: 2.5892 Epoch 90/1000 7/7 [==============================] - 0s 21ms/step - loss: 4.8983 - mean_absolute_error: 1.6560 - val_loss: 12.7878 - val_mean_absolute_error: 2.6190 Epoch 91/1000 7/7 [==============================] - 0s 22ms/step - loss: 5.4589 - mean_absolute_error: 1.7004 - val_loss: 13.1430 - val_mean_absolute_error: 2.6419 Epoch 92/1000 7/7 [==============================] - 0s 26ms/step - loss: 5.0386 - mean_absolute_error: 1.6907 - val_loss: 11.3990 - val_mean_absolute_error: 2.4243 Epoch 93/1000 7/7 [==============================] - 0s 26ms/step - loss: 5.0478 - mean_absolute_error: 1.6749 - val_loss: 13.6774 - val_mean_absolute_error: 2.7554 Epoch 94/1000 7/7 [==============================] - 0s 24ms/step - loss: 4.9821 - mean_absolute_error: 1.6601 - val_loss: 12.8103 - val_mean_absolute_error: 2.6776 Epoch 95/1000 7/7 [==============================] - 0s 22ms/step - loss: 4.7534 - mean_absolute_error: 1.6283 - val_loss: 11.9806 - val_mean_absolute_error: 2.5470 Epoch 96/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.9204 - mean_absolute_error: 1.6358 - val_loss: 12.0236 - val_mean_absolute_error: 2.4582 Epoch 97/1000 7/7 [==============================] - 0s 19ms/step - loss: 5.0187 - mean_absolute_error: 1.6001 - val_loss: 13.1244 - val_mean_absolute_error: 2.7101 Epoch 98/1000 7/7 [==============================] - 0s 18ms/step - loss: 4.8168 - mean_absolute_error: 1.6541 - val_loss: 12.2610 - val_mean_absolute_error: 2.5396 Epoch 99/1000 7/7 [==============================] - 0s 20ms/step - loss: 4.8922 - mean_absolute_error: 1.6271 - val_loss: 12.1913 - val_mean_absolute_error: 2.5053 Epoch 100/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.4846 - mean_absolute_error: 1.5753 - val_loss: 12.7141 - val_mean_absolute_error: 2.5651 Epoch 101/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.4748 - mean_absolute_error: 1.5224 - val_loss: 12.9386 - val_mean_absolute_error: 2.5972 Epoch 102/1000 7/7 [==============================] - 0s 18ms/step - loss: 4.3134 - mean_absolute_error: 1.5033 - val_loss: 12.2761 - val_mean_absolute_error: 2.5157 Epoch 103/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.5615 - mean_absolute_error: 1.5840 - val_loss: 12.5085 - val_mean_absolute_error: 2.6141 Epoch 104/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.4717 - mean_absolute_error: 1.5910 - val_loss: 12.7984 - val_mean_absolute_error: 2.5784 Epoch 105/1000 7/7 [==============================] - 0s 18ms/step - loss: 4.7943 - mean_absolute_error: 1.6286 - val_loss: 12.9117 - val_mean_absolute_error: 2.5846 Epoch 106/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.4746 - mean_absolute_error: 1.5722 - val_loss: 11.9590 - val_mean_absolute_error: 2.5496 Epoch 107/1000 7/7 [==============================] - 0s 18ms/step - loss: 4.3978 - mean_absolute_error: 1.5629 - val_loss: 12.5658 - val_mean_absolute_error: 2.5899 Epoch 108/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.5448 - mean_absolute_error: 1.5561 - val_loss: 12.5212 - val_mean_absolute_error: 2.5496 Epoch 109/1000 7/7 [==============================] - 0s 18ms/step - loss: 4.7927 - mean_absolute_error: 1.6331 - val_loss: 11.6538 - val_mean_absolute_error: 2.4354 Epoch 110/1000 7/7 [==============================] - 0s 20ms/step - loss: 4.4088 - mean_absolute_error: 1.5389 - val_loss: 13.2356 - val_mean_absolute_error: 2.6646 Epoch 111/1000 7/7 [==============================] - 0s 18ms/step - loss: 4.2269 - mean_absolute_error: 1.4840 - val_loss: 12.8991 - val_mean_absolute_error: 2.6198 Epoch 112/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.2605 - mean_absolute_error: 1.4988 - val_loss: 12.9676 - val_mean_absolute_error: 2.6025 Epoch 113/1000 7/7 [==============================] - 0s 18ms/step - loss: 4.1807 - mean_absolute_error: 1.4784 - val_loss: 12.1279 - val_mean_absolute_error: 2.5262 Epoch 114/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.0636 - mean_absolute_error: 1.4303 - val_loss: 12.3803 - val_mean_absolute_error: 2.6003 Epoch 115/1000 7/7 [==============================] - 0s 18ms/step - loss: 4.0812 - mean_absolute_error: 1.4412 - val_loss: 12.7715 - val_mean_absolute_error: 2.6361 Epoch 116/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.0374 - mean_absolute_error: 1.4427 - val_loss: 12.8340 - val_mean_absolute_error: 2.6313 Epoch 117/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.0925 - mean_absolute_error: 1.4380 - val_loss: 13.3917 - val_mean_absolute_error: 2.6874 Epoch 118/1000 7/7 [==============================] - 0s 20ms/step - loss: 4.5724 - mean_absolute_error: 1.5534 - val_loss: 13.0678 - val_mean_absolute_error: 2.7240 Epoch 119/1000 7/7 [==============================] - 0s 23ms/step - loss: 5.3190 - mean_absolute_error: 1.6742 - val_loss: 10.8514 - val_mean_absolute_error: 2.4352 Epoch 120/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.7900 - mean_absolute_error: 1.6292 - val_loss: 10.7903 - val_mean_absolute_error: 2.3793 Epoch 121/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.3578 - mean_absolute_error: 1.5544 - val_loss: 12.8346 - val_mean_absolute_error: 2.6465 Epoch 122/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.1827 - mean_absolute_error: 1.4880 - val_loss: 11.9892 - val_mean_absolute_error: 2.5906 Epoch 123/1000 7/7 [==============================] - 0s 19ms/step - loss: 4.0604 - mean_absolute_error: 1.4487 - val_loss: 11.8953 - val_mean_absolute_error: 2.5018 Epoch 124/1000 7/7 [==============================] - 0s 18ms/step - loss: 3.9625 - mean_absolute_error: 1.4475 - val_loss: 12.4267 - val_mean_absolute_error: 2.5983 Epoch 125/1000 7/7 [==============================] - 0s 20ms/step - loss: 3.9370 - mean_absolute_error: 1.4467 - val_loss: 12.3596 - val_mean_absolute_error: 2.6043 Epoch 126/1000 7/7 [==============================] - 0s 18ms/step - loss: 3.9902 - mean_absolute_error: 1.4342 - val_loss: 13.3795 - val_mean_absolute_error: 2.7398 Epoch 127/1000 7/7 [==============================] - 0s 20ms/step - loss: 3.8220 - mean_absolute_error: 1.3993 - val_loss: 12.0435 - val_mean_absolute_error: 2.5931 Epoch 128/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.8076 - mean_absolute_error: 1.3764 - val_loss: 12.6425 - val_mean_absolute_error: 2.6172 Epoch 129/1000 7/7 [==============================] - 0s 20ms/step - loss: 3.9072 - mean_absolute_error: 1.4311 - val_loss: 12.7946 - val_mean_absolute_error: 2.6661 Epoch 130/1000 7/7 [==============================] - 0s 20ms/step - loss: 3.6822 - mean_absolute_error: 1.3425 - val_loss: 12.9398 - val_mean_absolute_error: 2.6344 Epoch 131/1000 7/7 [==============================] - 0s 20ms/step - loss: 3.5976 - mean_absolute_error: 1.3208 - val_loss: 13.3665 - val_mean_absolute_error: 2.7415 Epoch 132/1000 7/7 [==============================] - 0s 18ms/step - loss: 3.6254 - mean_absolute_error: 1.3508 - val_loss: 12.8389 - val_mean_absolute_error: 2.6432 Epoch 133/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.7661 - mean_absolute_error: 1.3654 - val_loss: 12.5308 - val_mean_absolute_error: 2.5510 Epoch 134/1000 7/7 [==============================] - 0s 32ms/step - loss: 3.7588 - mean_absolute_error: 1.4257 - val_loss: 12.1947 - val_mean_absolute_error: 2.5241 Epoch 135/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.7844 - mean_absolute_error: 1.4215 - val_loss: 13.2723 - val_mean_absolute_error: 2.6925 Epoch 136/1000 7/7 [==============================] - 0s 20ms/step - loss: 3.7948 - mean_absolute_error: 1.4030 - val_loss: 12.9800 - val_mean_absolute_error: 2.6816 Epoch 137/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.7212 - mean_absolute_error: 1.3742 - val_loss: 12.1198 - val_mean_absolute_error: 2.6034 Epoch 138/1000 7/7 [==============================] - 0s 31ms/step - loss: 3.5539 - mean_absolute_error: 1.3352 - val_loss: 13.6189 - val_mean_absolute_error: 2.7744 Epoch 139/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.4228 - mean_absolute_error: 1.3061 - val_loss: 11.9345 - val_mean_absolute_error: 2.5933 Epoch 140/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.4839 - mean_absolute_error: 1.3042 - val_loss: 12.4128 - val_mean_absolute_error: 2.5863 Epoch 141/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.3998 - mean_absolute_error: 1.3211 - val_loss: 12.9665 - val_mean_absolute_error: 2.6662 Epoch 142/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.3544 - mean_absolute_error: 1.3186 - val_loss: 13.4429 - val_mean_absolute_error: 2.7686 Epoch 143/1000 7/7 [==============================] - 0s 20ms/step - loss: 3.2953 - mean_absolute_error: 1.2975 - val_loss: 12.5163 - val_mean_absolute_error: 2.5821 Epoch 144/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.5624 - mean_absolute_error: 1.3875 - val_loss: 13.1517 - val_mean_absolute_error: 2.7119 Epoch 145/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.4252 - mean_absolute_error: 1.3155 - val_loss: 13.5772 - val_mean_absolute_error: 2.7827 Epoch 146/1000 7/7 [==============================] - 0s 20ms/step - loss: 3.3175 - mean_absolute_error: 1.3082 - val_loss: 11.6494 - val_mean_absolute_error: 2.4498 Epoch 147/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.6939 - mean_absolute_error: 1.4229 - val_loss: 13.5035 - val_mean_absolute_error: 2.8318 Epoch 148/1000 7/7 [==============================] - 0s 20ms/step - loss: 3.2595 - mean_absolute_error: 1.3223 - val_loss: 12.9898 - val_mean_absolute_error: 2.6285 Epoch 149/1000 7/7 [==============================] - 0s 18ms/step - loss: 3.3602 - mean_absolute_error: 1.3630 - val_loss: 12.3865 - val_mean_absolute_error: 2.6420 Epoch 150/1000 7/7 [==============================] - 0s 20ms/step - loss: 3.3373 - mean_absolute_error: 1.3661 - val_loss: 13.0974 - val_mean_absolute_error: 2.6474 Epoch 151/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.6068 - mean_absolute_error: 1.4040 - val_loss: 13.1605 - val_mean_absolute_error: 2.8049 Epoch 152/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.6816 - mean_absolute_error: 1.3840 - val_loss: 12.2167 - val_mean_absolute_error: 2.5752 Epoch 153/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.4484 - mean_absolute_error: 1.2914 - val_loss: 12.8059 - val_mean_absolute_error: 2.6432 Epoch 154/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.6312 - mean_absolute_error: 1.3836 - val_loss: 11.0081 - val_mean_absolute_error: 2.4339 Epoch 155/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.4939 - mean_absolute_error: 1.3642 - val_loss: 14.0373 - val_mean_absolute_error: 2.8023 Epoch 156/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.2330 - mean_absolute_error: 1.2975 - val_loss: 12.0166 - val_mean_absolute_error: 2.5226 Epoch 157/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.2356 - mean_absolute_error: 1.3089 - val_loss: 11.7925 - val_mean_absolute_error: 2.6520 Epoch 158/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.7263 - mean_absolute_error: 1.4211 - val_loss: 11.8615 - val_mean_absolute_error: 2.6078 Epoch 159/1000 7/7 [==============================] - 0s 18ms/step - loss: 3.5285 - mean_absolute_error: 1.3405 - val_loss: 11.1144 - val_mean_absolute_error: 2.5904 Epoch 160/1000 7/7 [==============================] - 0s 18ms/step - loss: 3.4357 - mean_absolute_error: 1.3388 - val_loss: 13.3690 - val_mean_absolute_error: 2.7391 Epoch 161/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.2339 - mean_absolute_error: 1.3087 - val_loss: 12.3865 - val_mean_absolute_error: 2.6297 Epoch 162/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.2275 - mean_absolute_error: 1.2518 - val_loss: 12.5710 - val_mean_absolute_error: 2.6783 Epoch 163/1000 7/7 [==============================] - 0s 20ms/step - loss: 2.9112 - mean_absolute_error: 1.2018 - val_loss: 12.8073 - val_mean_absolute_error: 2.6621 Epoch 164/1000 7/7 [==============================] - 0s 20ms/step - loss: 2.8290 - mean_absolute_error: 1.1839 - val_loss: 12.1106 - val_mean_absolute_error: 2.6450 Epoch 165/1000 7/7 [==============================] - 0s 20ms/step - loss: 2.9259 - mean_absolute_error: 1.1866 - val_loss: 12.4660 - val_mean_absolute_error: 2.6539 Epoch 166/1000 7/7 [==============================] - 0s 20ms/step - loss: 2.6875 - mean_absolute_error: 1.1252 - val_loss: 12.6145 - val_mean_absolute_error: 2.7030 Epoch 167/1000 7/7 [==============================] - 0s 18ms/step - loss: 2.5966 - mean_absolute_error: 1.1212 - val_loss: 12.6631 - val_mean_absolute_error: 2.6601 Epoch 168/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.8397 - mean_absolute_error: 1.2279 - val_loss: 12.5011 - val_mean_absolute_error: 2.7734 Epoch 169/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.2086 - mean_absolute_error: 1.2937 - val_loss: 12.3550 - val_mean_absolute_error: 2.5688 Epoch 170/1000 7/7 [==============================] - 0s 20ms/step - loss: 3.0148 - mean_absolute_error: 1.2469 - val_loss: 14.2958 - val_mean_absolute_error: 2.8973 Epoch 171/1000 7/7 [==============================] - 0s 20ms/step - loss: 2.8080 - mean_absolute_error: 1.2321 - val_loss: 11.6233 - val_mean_absolute_error: 2.4659 Epoch 172/1000 7/7 [==============================] - 0s 22ms/step - loss: 2.9393 - mean_absolute_error: 1.2530 - val_loss: 12.5321 - val_mean_absolute_error: 2.7078 Epoch 173/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.0991 - mean_absolute_error: 1.2047 - val_loss: 12.5364 - val_mean_absolute_error: 2.7058 Epoch 174/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.8899 - mean_absolute_error: 1.2079 - val_loss: 11.8117 - val_mean_absolute_error: 2.5581 Epoch 175/1000 7/7 [==============================] - 0s 20ms/step - loss: 2.8351 - mean_absolute_error: 1.1899 - val_loss: 12.9878 - val_mean_absolute_error: 2.7698 Epoch 176/1000 7/7 [==============================] - 0s 20ms/step - loss: 2.9249 - mean_absolute_error: 1.1854 - val_loss: 12.2197 - val_mean_absolute_error: 2.6325 Epoch 177/1000 7/7 [==============================] - 0s 18ms/step - loss: 3.5959 - mean_absolute_error: 1.3331 - val_loss: 11.0748 - val_mean_absolute_error: 2.5442 Epoch 178/1000 7/7 [==============================] - 0s 20ms/step - loss: 3.2349 - mean_absolute_error: 1.3051 - val_loss: 13.5277 - val_mean_absolute_error: 2.7539 Epoch 179/1000 7/7 [==============================] - 0s 18ms/step - loss: 3.3604 - mean_absolute_error: 1.3233 - val_loss: 14.6791 - val_mean_absolute_error: 2.8941 Epoch 180/1000 7/7 [==============================] - 0s 20ms/step - loss: 3.1522 - mean_absolute_error: 1.2955 - val_loss: 12.1551 - val_mean_absolute_error: 2.6451 Epoch 181/1000 7/7 [==============================] - 0s 20ms/step - loss: 3.1939 - mean_absolute_error: 1.3001 - val_loss: 13.2004 - val_mean_absolute_error: 2.8204 Epoch 182/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.7504 - mean_absolute_error: 1.1616 - val_loss: 12.1828 - val_mean_absolute_error: 2.6402 Epoch 183/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.0820 - mean_absolute_error: 1.1765 - val_loss: 13.2203 - val_mean_absolute_error: 2.7731 Epoch 184/1000 7/7 [==============================] - 0s 21ms/step - loss: 2.7338 - mean_absolute_error: 1.1463 - val_loss: 12.4024 - val_mean_absolute_error: 2.6232 Epoch 185/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.9204 - mean_absolute_error: 1.1880 - val_loss: 12.0039 - val_mean_absolute_error: 2.5965 Epoch 186/1000 7/7 [==============================] - 0s 19ms/step - loss: 3.0240 - mean_absolute_error: 1.1721 - val_loss: 13.5304 - val_mean_absolute_error: 2.8214 Epoch 187/1000 7/7 [==============================] - 0s 18ms/step - loss: 2.7981 - mean_absolute_error: 1.1683 - val_loss: 11.4403 - val_mean_absolute_error: 2.5787 Epoch 188/1000 7/7 [==============================] - 0s 21ms/step - loss: 2.8102 - mean_absolute_error: 1.1634 - val_loss: 13.3523 - val_mean_absolute_error: 2.7992 Epoch 189/1000 7/7 [==============================] - 0s 29ms/step - loss: 2.5213 - mean_absolute_error: 1.0929 - val_loss: 12.5005 - val_mean_absolute_error: 2.7203 Epoch 190/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.5505 - mean_absolute_error: 1.0848 - val_loss: 12.4204 - val_mean_absolute_error: 2.7069 Epoch 191/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.5942 - mean_absolute_error: 1.0935 - val_loss: 13.1597 - val_mean_absolute_error: 2.7577 Epoch 192/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.5254 - mean_absolute_error: 1.0903 - val_loss: 12.2041 - val_mean_absolute_error: 2.6652 Epoch 193/1000 7/7 [==============================] - 0s 20ms/step - loss: 2.4851 - mean_absolute_error: 1.0758 - val_loss: 12.6433 - val_mean_absolute_error: 2.7398 Epoch 194/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.4414 - mean_absolute_error: 1.0455 - val_loss: 12.6697 - val_mean_absolute_error: 2.7087 Epoch 195/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.5706 - mean_absolute_error: 1.0616 - val_loss: 13.2566 - val_mean_absolute_error: 2.7952 Epoch 196/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.5779 - mean_absolute_error: 1.1351 - val_loss: 11.7363 - val_mean_absolute_error: 2.6235 Epoch 197/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.9422 - mean_absolute_error: 1.1798 - val_loss: 12.2922 - val_mean_absolute_error: 2.6092 Epoch 198/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.7394 - mean_absolute_error: 1.1256 - val_loss: 13.2882 - val_mean_absolute_error: 2.8366 Epoch 199/1000 7/7 [==============================] - 0s 20ms/step - loss: 2.4540 - mean_absolute_error: 1.0731 - val_loss: 11.7355 - val_mean_absolute_error: 2.6240 Epoch 200/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.3097 - mean_absolute_error: 0.9956 - val_loss: 12.9903 - val_mean_absolute_error: 2.7603 Epoch 201/1000 7/7 [==============================] - 0s 18ms/step - loss: 2.2802 - mean_absolute_error: 0.9937 - val_loss: 11.9330 - val_mean_absolute_error: 2.6054 Epoch 202/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.2249 - mean_absolute_error: 0.9767 - val_loss: 13.6016 - val_mean_absolute_error: 2.8245 Epoch 203/1000 7/7 [==============================] - 0s 18ms/step - loss: 2.3221 - mean_absolute_error: 1.0519 - val_loss: 12.3704 - val_mean_absolute_error: 2.6876 Epoch 204/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.4337 - mean_absolute_error: 1.0849 - val_loss: 13.3206 - val_mean_absolute_error: 2.8483 Epoch 205/1000 7/7 [==============================] - 0s 20ms/step - loss: 2.3430 - mean_absolute_error: 1.0738 - val_loss: 11.8012 - val_mean_absolute_error: 2.6121 Epoch 206/1000 7/7 [==============================] - 0s 18ms/step - loss: 2.2496 - mean_absolute_error: 1.0179 - val_loss: 12.6676 - val_mean_absolute_error: 2.7548 Epoch 207/1000 7/7 [==============================] - 0s 29ms/step - loss: 2.1550 - mean_absolute_error: 0.9799 - val_loss: 12.4762 - val_mean_absolute_error: 2.7025 Epoch 208/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.0954 - mean_absolute_error: 0.9359 - val_loss: 12.6444 - val_mean_absolute_error: 2.6873 Epoch 209/1000 7/7 [==============================] - 0s 18ms/step - loss: 2.0372 - mean_absolute_error: 0.9131 - val_loss: 12.6689 - val_mean_absolute_error: 2.7301 Epoch 210/1000 7/7 [==============================] - 0s 20ms/step - loss: 2.0144 - mean_absolute_error: 0.8845 - val_loss: 12.7219 - val_mean_absolute_error: 2.7553 Epoch 211/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.8752 - mean_absolute_error: 0.8527 - val_loss: 12.5695 - val_mean_absolute_error: 2.7437 Epoch 212/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.0249 - mean_absolute_error: 0.9187 - val_loss: 13.0968 - val_mean_absolute_error: 2.7925 Epoch 213/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.8433 - mean_absolute_error: 0.8664 - val_loss: 12.4806 - val_mean_absolute_error: 2.7352 Epoch 214/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.0824 - mean_absolute_error: 0.9203 - val_loss: 12.5848 - val_mean_absolute_error: 2.7478 Epoch 215/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.1226 - mean_absolute_error: 0.9491 - val_loss: 13.0096 - val_mean_absolute_error: 2.7732 Epoch 216/1000 7/7 [==============================] - 0s 18ms/step - loss: 2.0126 - mean_absolute_error: 0.8887 - val_loss: 13.1353 - val_mean_absolute_error: 2.7888 Epoch 217/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.9563 - mean_absolute_error: 0.8919 - val_loss: 12.8032 - val_mean_absolute_error: 2.8377 Epoch 218/1000 7/7 [==============================] - 0s 20ms/step - loss: 2.0272 - mean_absolute_error: 0.9038 - val_loss: 13.9299 - val_mean_absolute_error: 2.9011 Epoch 219/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.8432 - mean_absolute_error: 0.8343 - val_loss: 12.9312 - val_mean_absolute_error: 2.8020 Epoch 220/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.8907 - mean_absolute_error: 0.8689 - val_loss: 12.2918 - val_mean_absolute_error: 2.7197 Epoch 221/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.8501 - mean_absolute_error: 0.8429 - val_loss: 12.9125 - val_mean_absolute_error: 2.7686 Epoch 222/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.9082 - mean_absolute_error: 0.8791 - val_loss: 13.3334 - val_mean_absolute_error: 2.8561 Epoch 223/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.9930 - mean_absolute_error: 0.9110 - val_loss: 12.6594 - val_mean_absolute_error: 2.8168 Epoch 224/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.8216 - mean_absolute_error: 0.8646 - val_loss: 12.8373 - val_mean_absolute_error: 2.7311 Epoch 225/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.7863 - mean_absolute_error: 0.8227 - val_loss: 12.7251 - val_mean_absolute_error: 2.8176 Epoch 226/1000 7/7 [==============================] - 0s 21ms/step - loss: 1.7250 - mean_absolute_error: 0.8228 - val_loss: 12.6748 - val_mean_absolute_error: 2.7820 Epoch 227/1000 7/7 [==============================] - 0s 25ms/step - loss: 1.6162 - mean_absolute_error: 0.7703 - val_loss: 12.5986 - val_mean_absolute_error: 2.7811 Epoch 228/1000 7/7 [==============================] - 0s 28ms/step - loss: 1.7073 - mean_absolute_error: 0.7871 - val_loss: 13.0593 - val_mean_absolute_error: 2.7875 Epoch 229/1000 7/7 [==============================] - 0s 22ms/step - loss: 1.6276 - mean_absolute_error: 0.7658 - val_loss: 12.3247 - val_mean_absolute_error: 2.7630 Epoch 230/1000 7/7 [==============================] - 0s 22ms/step - loss: 1.6606 - mean_absolute_error: 0.7907 - val_loss: 12.9973 - val_mean_absolute_error: 2.8289 Epoch 231/1000 7/7 [==============================] - 0s 25ms/step - loss: 1.6364 - mean_absolute_error: 0.7740 - val_loss: 12.8169 - val_mean_absolute_error: 2.7883 Epoch 232/1000 7/7 [==============================] - 0s 22ms/step - loss: 1.6634 - mean_absolute_error: 0.8055 - val_loss: 11.8988 - val_mean_absolute_error: 2.6359 Epoch 233/1000 7/7 [==============================] - 0s 21ms/step - loss: 1.8246 - mean_absolute_error: 0.8452 - val_loss: 12.9469 - val_mean_absolute_error: 2.8024 Epoch 234/1000 7/7 [==============================] - 0s 24ms/step - loss: 1.7502 - mean_absolute_error: 0.8380 - val_loss: 12.9357 - val_mean_absolute_error: 2.7950 Epoch 235/1000 7/7 [==============================] - 0s 22ms/step - loss: 2.0699 - mean_absolute_error: 0.9305 - val_loss: 12.1917 - val_mean_absolute_error: 2.7349 Epoch 236/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.9253 - mean_absolute_error: 0.8379 - val_loss: 13.6970 - val_mean_absolute_error: 2.8810 Epoch 237/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.7554 - mean_absolute_error: 0.7932 - val_loss: 13.4254 - val_mean_absolute_error: 2.8723 Epoch 238/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.6954 - mean_absolute_error: 0.7541 - val_loss: 12.6958 - val_mean_absolute_error: 2.7500 Epoch 239/1000 7/7 [==============================] - 0s 29ms/step - loss: 1.7547 - mean_absolute_error: 0.7648 - val_loss: 12.9633 - val_mean_absolute_error: 2.7602 Epoch 240/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.7530 - mean_absolute_error: 0.7943 - val_loss: 13.1979 - val_mean_absolute_error: 2.8781 Epoch 241/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.7037 - mean_absolute_error: 0.7861 - val_loss: 13.1071 - val_mean_absolute_error: 2.8029 Epoch 242/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.9248 - mean_absolute_error: 0.8296 - val_loss: 13.0066 - val_mean_absolute_error: 2.8164 Epoch 243/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.5882 - mean_absolute_error: 0.7459 - val_loss: 12.6333 - val_mean_absolute_error: 2.7319 Epoch 244/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.8606 - mean_absolute_error: 0.8263 - val_loss: 13.0265 - val_mean_absolute_error: 2.8511 Epoch 245/1000 7/7 [==============================] - 0s 22ms/step - loss: 1.8530 - mean_absolute_error: 0.8601 - val_loss: 13.3213 - val_mean_absolute_error: 2.8676 Epoch 246/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.7456 - mean_absolute_error: 0.7971 - val_loss: 13.4899 - val_mean_absolute_error: 2.8914 Epoch 247/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.6925 - mean_absolute_error: 0.7906 - val_loss: 13.3816 - val_mean_absolute_error: 2.8236 Epoch 248/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.5968 - mean_absolute_error: 0.7499 - val_loss: 12.8973 - val_mean_absolute_error: 2.7539 Epoch 249/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.5786 - mean_absolute_error: 0.7472 - val_loss: 14.1887 - val_mean_absolute_error: 2.9147 Epoch 250/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.5852 - mean_absolute_error: 0.7595 - val_loss: 12.6440 - val_mean_absolute_error: 2.7761 Epoch 251/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.7665 - mean_absolute_error: 0.7582 - val_loss: 15.3928 - val_mean_absolute_error: 3.0514 Epoch 252/1000 7/7 [==============================] - 0s 18ms/step - loss: 2.6436 - mean_absolute_error: 1.0523 - val_loss: 11.2492 - val_mean_absolute_error: 2.2380 Epoch 253/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.4847 - mean_absolute_error: 1.0604 - val_loss: 16.6632 - val_mean_absolute_error: 3.1085 Epoch 254/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.4676 - mean_absolute_error: 1.0815 - val_loss: 12.8716 - val_mean_absolute_error: 2.5552 Epoch 255/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.4281 - mean_absolute_error: 1.0794 - val_loss: 14.3597 - val_mean_absolute_error: 2.9785 Epoch 256/1000 7/7 [==============================] - 0s 20ms/step - loss: 2.3006 - mean_absolute_error: 1.0966 - val_loss: 12.3875 - val_mean_absolute_error: 2.7427 Epoch 257/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.9518 - mean_absolute_error: 0.9106 - val_loss: 13.8281 - val_mean_absolute_error: 2.9135 Epoch 258/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.9384 - mean_absolute_error: 0.9405 - val_loss: 13.3481 - val_mean_absolute_error: 2.8067 Epoch 259/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.7486 - mean_absolute_error: 0.8737 - val_loss: 12.6535 - val_mean_absolute_error: 2.7880 Epoch 260/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.7311 - mean_absolute_error: 0.8479 - val_loss: 12.7116 - val_mean_absolute_error: 2.7596 Epoch 261/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.5309 - mean_absolute_error: 0.8201 - val_loss: 13.3832 - val_mean_absolute_error: 2.7163 Epoch 262/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.4727 - mean_absolute_error: 0.8042 - val_loss: 13.7870 - val_mean_absolute_error: 2.8969 Epoch 263/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.5041 - mean_absolute_error: 0.8090 - val_loss: 14.2560 - val_mean_absolute_error: 2.9444 Epoch 264/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.6761 - mean_absolute_error: 0.8667 - val_loss: 13.2164 - val_mean_absolute_error: 2.8325 Epoch 265/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.7323 - mean_absolute_error: 0.8954 - val_loss: 14.3250 - val_mean_absolute_error: 2.7647 Epoch 266/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.0753 - mean_absolute_error: 1.0224 - val_loss: 16.0102 - val_mean_absolute_error: 3.0531 Epoch 267/1000 7/7 [==============================] - 0s 17ms/step - loss: 3.5566 - mean_absolute_error: 1.1571 - val_loss: 11.5109 - val_mean_absolute_error: 2.6468 Epoch 268/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.6104 - mean_absolute_error: 1.0778 - val_loss: 14.2485 - val_mean_absolute_error: 2.9920 Epoch 269/1000 7/7 [==============================] - 0s 21ms/step - loss: 2.4153 - mean_absolute_error: 1.0305 - val_loss: 13.8838 - val_mean_absolute_error: 2.7478 Epoch 270/1000 7/7 [==============================] - 0s 21ms/step - loss: 2.8822 - mean_absolute_error: 1.1091 - val_loss: 14.7938 - val_mean_absolute_error: 2.9374 Epoch 271/1000 7/7 [==============================] - 0s 18ms/step - loss: 2.1587 - mean_absolute_error: 0.9958 - val_loss: 13.1778 - val_mean_absolute_error: 2.7403 Epoch 272/1000 7/7 [==============================] - 0s 20ms/step - loss: 2.1483 - mean_absolute_error: 0.9179 - val_loss: 13.2122 - val_mean_absolute_error: 2.7547 Epoch 273/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.2818 - mean_absolute_error: 1.0370 - val_loss: 13.6394 - val_mean_absolute_error: 2.8327 Epoch 274/1000 7/7 [==============================] - 0s 18ms/step - loss: 2.1725 - mean_absolute_error: 0.9710 - val_loss: 13.1538 - val_mean_absolute_error: 2.8590 Epoch 275/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.8247 - mean_absolute_error: 0.8524 - val_loss: 13.2541 - val_mean_absolute_error: 2.7563 Epoch 276/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.6077 - mean_absolute_error: 0.7383 - val_loss: 13.8004 - val_mean_absolute_error: 2.8290 Epoch 277/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.7312 - mean_absolute_error: 0.7981 - val_loss: 13.2351 - val_mean_absolute_error: 2.7254 Epoch 278/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.6136 - mean_absolute_error: 0.7733 - val_loss: 13.3115 - val_mean_absolute_error: 2.8249 Epoch 279/1000 7/7 [==============================] - 0s 22ms/step - loss: 1.6583 - mean_absolute_error: 0.7662 - val_loss: 12.8859 - val_mean_absolute_error: 2.7544 Epoch 280/1000 7/7 [==============================] - 0s 26ms/step - loss: 1.5559 - mean_absolute_error: 0.7360 - val_loss: 13.7534 - val_mean_absolute_error: 2.8384 Epoch 281/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.4118 - mean_absolute_error: 0.6753 - val_loss: 13.2601 - val_mean_absolute_error: 2.7630 Epoch 282/1000 7/7 [==============================] - 0s 22ms/step - loss: 1.4824 - mean_absolute_error: 0.6904 - val_loss: 13.5435 - val_mean_absolute_error: 2.8643 Epoch 283/1000 7/7 [==============================] - 0s 23ms/step - loss: 1.5795 - mean_absolute_error: 0.7180 - val_loss: 13.5561 - val_mean_absolute_error: 2.7581 Epoch 284/1000 7/7 [==============================] - 0s 23ms/step - loss: 1.4854 - mean_absolute_error: 0.7208 - val_loss: 13.6724 - val_mean_absolute_error: 2.8603 Epoch 285/1000 7/7 [==============================] - 0s 27ms/step - loss: 1.6637 - mean_absolute_error: 0.7499 - val_loss: 13.3771 - val_mean_absolute_error: 2.7196 Epoch 286/1000 7/7 [==============================] - 0s 22ms/step - loss: 1.5863 - mean_absolute_error: 0.7491 - val_loss: 14.0570 - val_mean_absolute_error: 2.8688 Epoch 287/1000 7/7 [==============================] - 0s 21ms/step - loss: 1.5165 - mean_absolute_error: 0.7567 - val_loss: 13.5832 - val_mean_absolute_error: 2.8444 Epoch 288/1000 7/7 [==============================] - 0s 21ms/step - loss: 1.3690 - mean_absolute_error: 0.6916 - val_loss: 13.6316 - val_mean_absolute_error: 2.7660 Epoch 289/1000 7/7 [==============================] - 0s 32ms/step - loss: 1.5876 - mean_absolute_error: 0.7639 - val_loss: 13.9952 - val_mean_absolute_error: 2.8315 Epoch 290/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.3575 - mean_absolute_error: 0.6775 - val_loss: 14.4714 - val_mean_absolute_error: 2.9061 Epoch 291/1000 7/7 [==============================] - 0s 21ms/step - loss: 1.3513 - mean_absolute_error: 0.6528 - val_loss: 13.5117 - val_mean_absolute_error: 2.8289 Epoch 292/1000 7/7 [==============================] - 0s 21ms/step - loss: 1.3392 - mean_absolute_error: 0.6707 - val_loss: 13.5360 - val_mean_absolute_error: 2.8021 Epoch 293/1000 7/7 [==============================] - 0s 24ms/step - loss: 1.3690 - mean_absolute_error: 0.6694 - val_loss: 13.8520 - val_mean_absolute_error: 2.8993 Epoch 294/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.2582 - mean_absolute_error: 0.6603 - val_loss: 13.0918 - val_mean_absolute_error: 2.7356 Epoch 295/1000 7/7 [==============================] - 0s 21ms/step - loss: 1.2206 - mean_absolute_error: 0.6054 - val_loss: 13.8794 - val_mean_absolute_error: 2.8254 Epoch 296/1000 7/7 [==============================] - 0s 22ms/step - loss: 1.2389 - mean_absolute_error: 0.6221 - val_loss: 13.6853 - val_mean_absolute_error: 2.7977 Epoch 297/1000 7/7 [==============================] - 0s 24ms/step - loss: 1.2270 - mean_absolute_error: 0.6087 - val_loss: 13.8615 - val_mean_absolute_error: 2.8586 Epoch 298/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.2495 - mean_absolute_error: 0.5869 - val_loss: 14.5329 - val_mean_absolute_error: 2.9059 Epoch 299/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.1675 - mean_absolute_error: 0.5855 - val_loss: 13.6526 - val_mean_absolute_error: 2.7747 Epoch 300/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.1377 - mean_absolute_error: 0.5649 - val_loss: 13.3976 - val_mean_absolute_error: 2.7875 Epoch 301/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.1762 - mean_absolute_error: 0.5837 - val_loss: 14.3869 - val_mean_absolute_error: 2.8643 Epoch 302/1000 7/7 [==============================] - 0s 21ms/step - loss: 1.0932 - mean_absolute_error: 0.5927 - val_loss: 14.1630 - val_mean_absolute_error: 2.8814 Epoch 303/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.1893 - mean_absolute_error: 0.5958 - val_loss: 14.1726 - val_mean_absolute_error: 2.8324 Epoch 304/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.1309 - mean_absolute_error: 0.5640 - val_loss: 14.2133 - val_mean_absolute_error: 2.8910 Epoch 305/1000 7/7 [==============================] - 0s 21ms/step - loss: 1.0972 - mean_absolute_error: 0.5422 - val_loss: 14.0653 - val_mean_absolute_error: 2.8691 Epoch 306/1000 7/7 [==============================] - 0s 22ms/step - loss: 1.0617 - mean_absolute_error: 0.5565 - val_loss: 13.6935 - val_mean_absolute_error: 2.7799 Epoch 307/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.0307 - mean_absolute_error: 0.5328 - val_loss: 14.3209 - val_mean_absolute_error: 2.8879 Epoch 308/1000 7/7 [==============================] - 0s 21ms/step - loss: 1.0901 - mean_absolute_error: 0.5722 - val_loss: 13.7748 - val_mean_absolute_error: 2.8257 Epoch 309/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.2835 - mean_absolute_error: 0.6110 - val_loss: 14.4872 - val_mean_absolute_error: 2.8737 Epoch 310/1000 7/7 [==============================] - 0s 25ms/step - loss: 1.0164 - mean_absolute_error: 0.5330 - val_loss: 13.7061 - val_mean_absolute_error: 2.7780 Epoch 311/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.1341 - mean_absolute_error: 0.5895 - val_loss: 13.3623 - val_mean_absolute_error: 2.8006 Epoch 312/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.1567 - mean_absolute_error: 0.5836 - val_loss: 13.3512 - val_mean_absolute_error: 2.7428 Epoch 313/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.1698 - mean_absolute_error: 0.6427 - val_loss: 13.8873 - val_mean_absolute_error: 2.8497 Epoch 314/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.4565 - mean_absolute_error: 0.7274 - val_loss: 14.5224 - val_mean_absolute_error: 2.8855 Epoch 315/1000 7/7 [==============================] - 0s 22ms/step - loss: 1.1561 - mean_absolute_error: 0.6642 - val_loss: 12.9108 - val_mean_absolute_error: 2.6909 Epoch 316/1000 7/7 [==============================] - 0s 22ms/step - loss: 1.0418 - mean_absolute_error: 0.5985 - val_loss: 14.6673 - val_mean_absolute_error: 2.9015 Epoch 317/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.0734 - mean_absolute_error: 0.5988 - val_loss: 13.3070 - val_mean_absolute_error: 2.7304 Epoch 318/1000 7/7 [==============================] - 0s 21ms/step - loss: 1.6485 - mean_absolute_error: 0.6775 - val_loss: 13.6793 - val_mean_absolute_error: 2.7573 Epoch 319/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.1262 - mean_absolute_error: 0.6671 - val_loss: 14.6938 - val_mean_absolute_error: 2.8498 Epoch 320/1000 7/7 [==============================] - 0s 21ms/step - loss: 1.2025 - mean_absolute_error: 0.6358 - val_loss: 14.1733 - val_mean_absolute_error: 2.7786 Epoch 321/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.0948 - mean_absolute_error: 0.6086 - val_loss: 13.8149 - val_mean_absolute_error: 2.7760 Epoch 322/1000 7/7 [==============================] - 0s 22ms/step - loss: 1.1640 - mean_absolute_error: 0.6445 - val_loss: 13.4273 - val_mean_absolute_error: 2.7303 Epoch 323/1000 7/7 [==============================] - 0s 22ms/step - loss: 1.1258 - mean_absolute_error: 0.6282 - val_loss: 14.5609 - val_mean_absolute_error: 2.9034 Epoch 324/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.2281 - mean_absolute_error: 0.6214 - val_loss: 14.9671 - val_mean_absolute_error: 2.9621 Epoch 325/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.1043 - mean_absolute_error: 0.6411 - val_loss: 12.9822 - val_mean_absolute_error: 2.7748 Epoch 326/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.1135 - mean_absolute_error: 0.6227 - val_loss: 14.2214 - val_mean_absolute_error: 2.8782 Epoch 327/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.3097 - mean_absolute_error: 0.6383 - val_loss: 14.1472 - val_mean_absolute_error: 2.8138 Epoch 328/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.9387 - mean_absolute_error: 0.5789 - val_loss: 13.5660 - val_mean_absolute_error: 2.7810 Epoch 329/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.9973 - mean_absolute_error: 0.5558 - val_loss: 14.4357 - val_mean_absolute_error: 2.8575 Epoch 330/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.9097 - mean_absolute_error: 0.5186 - val_loss: 13.9119 - val_mean_absolute_error: 2.7797 Epoch 331/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.8871 - mean_absolute_error: 0.4846 - val_loss: 14.3041 - val_mean_absolute_error: 2.8321 Epoch 332/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.8018 - mean_absolute_error: 0.4913 - val_loss: 13.4298 - val_mean_absolute_error: 2.7844 Epoch 333/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.8976 - mean_absolute_error: 0.5079 - val_loss: 14.3412 - val_mean_absolute_error: 2.8235 Epoch 334/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.9134 - mean_absolute_error: 0.5331 - val_loss: 13.6470 - val_mean_absolute_error: 2.7424 Epoch 335/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.9037 - mean_absolute_error: 0.5455 - val_loss: 14.7919 - val_mean_absolute_error: 2.8647 Epoch 336/1000 7/7 [==============================] - 0s 25ms/step - loss: 1.0885 - mean_absolute_error: 0.6295 - val_loss: 12.9366 - val_mean_absolute_error: 2.6790 Epoch 337/1000 7/7 [==============================] - 0s 31ms/step - loss: 1.4299 - mean_absolute_error: 0.7251 - val_loss: 13.5096 - val_mean_absolute_error: 2.7907 Epoch 338/1000 7/7 [==============================] - 0s 21ms/step - loss: 1.7901 - mean_absolute_error: 0.6984 - val_loss: 15.7702 - val_mean_absolute_error: 3.0417 Epoch 339/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.8892 - mean_absolute_error: 0.8307 - val_loss: 9.7160 - val_mean_absolute_error: 2.4850 Epoch 340/1000 7/7 [==============================] - 0s 21ms/step - loss: 3.0530 - mean_absolute_error: 1.0353 - val_loss: 11.6891 - val_mean_absolute_error: 2.6125 Epoch 341/1000 7/7 [==============================] - 0s 19ms/step - loss: 2.4976 - mean_absolute_error: 0.9743 - val_loss: 14.7312 - val_mean_absolute_error: 2.8207 Epoch 342/1000 7/7 [==============================] - 0s 20ms/step - loss: 2.3126 - mean_absolute_error: 0.9640 - val_loss: 12.3940 - val_mean_absolute_error: 2.6361 Epoch 343/1000 7/7 [==============================] - 0s 20ms/step - loss: 2.0863 - mean_absolute_error: 0.8921 - val_loss: 13.8851 - val_mean_absolute_error: 2.8386 Epoch 344/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.9182 - mean_absolute_error: 0.8678 - val_loss: 14.1725 - val_mean_absolute_error: 2.8768 Epoch 345/1000 7/7 [==============================] - 0s 21ms/step - loss: 1.8672 - mean_absolute_error: 0.8834 - val_loss: 12.5614 - val_mean_absolute_error: 2.6982 Epoch 346/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.7687 - mean_absolute_error: 0.8474 - val_loss: 13.7631 - val_mean_absolute_error: 2.8342 Epoch 347/1000 7/7 [==============================] - 0s 33ms/step - loss: 1.5782 - mean_absolute_error: 0.7540 - val_loss: 14.6501 - val_mean_absolute_error: 2.9250 Epoch 348/1000 7/7 [==============================] - 0s 21ms/step - loss: 1.8648 - mean_absolute_error: 0.8111 - val_loss: 13.1630 - val_mean_absolute_error: 2.7923 Epoch 349/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.7524 - mean_absolute_error: 0.7818 - val_loss: 13.9437 - val_mean_absolute_error: 2.8025 Epoch 350/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.5628 - mean_absolute_error: 0.7651 - val_loss: 14.9997 - val_mean_absolute_error: 2.9412 Epoch 351/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.5876 - mean_absolute_error: 0.7775 - val_loss: 13.0603 - val_mean_absolute_error: 2.7350 Epoch 352/1000 7/7 [==============================] - 0s 21ms/step - loss: 1.5235 - mean_absolute_error: 0.6856 - val_loss: 14.0974 - val_mean_absolute_error: 2.8067 Epoch 353/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.5972 - mean_absolute_error: 0.7224 - val_loss: 14.2348 - val_mean_absolute_error: 2.8421 Epoch 354/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.5455 - mean_absolute_error: 0.7323 - val_loss: 13.9039 - val_mean_absolute_error: 2.9202 Epoch 355/1000 7/7 [==============================] - 0s 23ms/step - loss: 1.8405 - mean_absolute_error: 0.8736 - val_loss: 14.0332 - val_mean_absolute_error: 2.8668 Epoch 356/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.6340 - mean_absolute_error: 0.7642 - val_loss: 15.0625 - val_mean_absolute_error: 2.9795 Epoch 357/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.5966 - mean_absolute_error: 0.7829 - val_loss: 13.4377 - val_mean_absolute_error: 2.8026 Epoch 358/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.4550 - mean_absolute_error: 0.7278 - val_loss: 14.4172 - val_mean_absolute_error: 2.9424 Epoch 359/1000 7/7 [==============================] - 0s 21ms/step - loss: 1.5410 - mean_absolute_error: 0.7166 - val_loss: 13.7839 - val_mean_absolute_error: 2.8876 Epoch 360/1000 7/7 [==============================] - 0s 21ms/step - loss: 1.2578 - mean_absolute_error: 0.6522 - val_loss: 13.8186 - val_mean_absolute_error: 2.8279 Epoch 361/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.2706 - mean_absolute_error: 0.6393 - val_loss: 14.2714 - val_mean_absolute_error: 2.8573 Epoch 362/1000 7/7 [==============================] - 0s 21ms/step - loss: 1.1000 - mean_absolute_error: 0.5736 - val_loss: 15.2553 - val_mean_absolute_error: 2.9900 Epoch 363/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.1161 - mean_absolute_error: 0.5431 - val_loss: 13.2519 - val_mean_absolute_error: 2.7151 Epoch 364/1000 7/7 [==============================] - 0s 27ms/step - loss: 1.1809 - mean_absolute_error: 0.5916 - val_loss: 14.5910 - val_mean_absolute_error: 2.9089 Epoch 365/1000 7/7 [==============================] - 0s 22ms/step - loss: 1.1678 - mean_absolute_error: 0.5615 - val_loss: 14.2992 - val_mean_absolute_error: 2.8417 Epoch 366/1000 7/7 [==============================] - 0s 25ms/step - loss: 1.3693 - mean_absolute_error: 0.6488 - val_loss: 14.1832 - val_mean_absolute_error: 2.8614 Epoch 367/1000 7/7 [==============================] - 0s 22ms/step - loss: 1.3630 - mean_absolute_error: 0.7004 - val_loss: 14.6123 - val_mean_absolute_error: 2.9136 Epoch 368/1000 7/7 [==============================] - 0s 22ms/step - loss: 1.1716 - mean_absolute_error: 0.6416 - val_loss: 13.8274 - val_mean_absolute_error: 2.7785 Epoch 369/1000 7/7 [==============================] - 0s 21ms/step - loss: 1.1518 - mean_absolute_error: 0.6028 - val_loss: 14.2313 - val_mean_absolute_error: 2.8424 Epoch 370/1000 7/7 [==============================] - 0s 23ms/step - loss: 1.1496 - mean_absolute_error: 0.6165 - val_loss: 14.5253 - val_mean_absolute_error: 2.8175 Epoch 371/1000 7/7 [==============================] - 0s 24ms/step - loss: 1.0095 - mean_absolute_error: 0.5228 - val_loss: 14.6922 - val_mean_absolute_error: 2.9221 Epoch 372/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.9653 - mean_absolute_error: 0.5187 - val_loss: 14.4280 - val_mean_absolute_error: 2.9082 Epoch 373/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.9717 - mean_absolute_error: 0.5183 - val_loss: 14.9278 - val_mean_absolute_error: 2.9631 Epoch 374/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.9388 - mean_absolute_error: 0.4895 - val_loss: 15.0855 - val_mean_absolute_error: 2.9526 Epoch 375/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.0196 - mean_absolute_error: 0.4786 - val_loss: 14.2870 - val_mean_absolute_error: 2.8511 Epoch 376/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.0046 - mean_absolute_error: 0.4874 - val_loss: 15.2791 - val_mean_absolute_error: 2.9519 Epoch 377/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.9403 - mean_absolute_error: 0.4750 - val_loss: 13.9751 - val_mean_absolute_error: 2.8271 Epoch 378/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.9603 - mean_absolute_error: 0.4784 - val_loss: 15.4989 - val_mean_absolute_error: 3.0383 Epoch 379/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.8918 - mean_absolute_error: 0.5081 - val_loss: 14.0860 - val_mean_absolute_error: 2.7875 Epoch 380/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.8656 - mean_absolute_error: 0.5118 - val_loss: 15.3849 - val_mean_absolute_error: 2.9299 Epoch 381/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.9830 - mean_absolute_error: 0.4985 - val_loss: 14.7046 - val_mean_absolute_error: 2.9066 Epoch 382/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.0010 - mean_absolute_error: 0.5209 - val_loss: 14.2985 - val_mean_absolute_error: 2.8841 Epoch 383/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.2449 - mean_absolute_error: 0.6060 - val_loss: 15.7971 - val_mean_absolute_error: 3.0291 Epoch 384/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.4486 - mean_absolute_error: 0.6038 - val_loss: 14.4146 - val_mean_absolute_error: 2.8174 Epoch 385/1000 7/7 [==============================] - 0s 18ms/step - loss: 1.1506 - mean_absolute_error: 0.5549 - val_loss: 17.5011 - val_mean_absolute_error: 3.1235 Epoch 386/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.9028 - mean_absolute_error: 0.5619 - val_loss: 14.1620 - val_mean_absolute_error: 2.8164 Epoch 387/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.8405 - mean_absolute_error: 0.5670 - val_loss: 17.2370 - val_mean_absolute_error: 3.1372 7/7 [==============================] - 0s 36ms/step - loss: 0.8711 - mean_absolute_error: 0.5621 - val_loss: 13.9237 - val_mean_absolute_error: 2.8079 Epoch 389/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.8334 - mean_absolute_error: 0.5465 - val_loss: 15.3046 - val_mean_absolute_error: 2.9634 Epoch 390/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.7745 - mean_absolute_error: 0.4858 - val_loss: 14.6222 - val_mean_absolute_error: 2.8539 Epoch 391/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.8387 - mean_absolute_error: 0.5398 - val_loss: 15.5661 - val_mean_absolute_error: 2.9980 Epoch 392/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.8682 - mean_absolute_error: 0.5776 - val_loss: 13.4078 - val_mean_absolute_error: 2.7300 Epoch 393/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.9807 - mean_absolute_error: 0.5753 - val_loss: 15.1904 - val_mean_absolute_error: 2.9547 Epoch 394/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.8236 - mean_absolute_error: 0.5432 - val_loss: 15.0293 - val_mean_absolute_error: 2.9383 Epoch 395/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.8343 - mean_absolute_error: 0.5228 - val_loss: 15.5731 - val_mean_absolute_error: 2.9793 Epoch 396/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.9012 - mean_absolute_error: 0.5648 - val_loss: 13.7559 - val_mean_absolute_error: 2.7668 Epoch 397/1000 7/7 [==============================] - 0s 16ms/step - loss: 0.9442 - mean_absolute_error: 0.5642 - val_loss: 15.9267 - val_mean_absolute_error: 3.0070 Epoch 398/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.9767 - mean_absolute_error: 0.6187 - val_loss: 14.3710 - val_mean_absolute_error: 2.8009 Epoch 399/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.9427 - mean_absolute_error: 0.5810 - val_loss: 14.9165 - val_mean_absolute_error: 3.0005 Epoch 400/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.9576 - mean_absolute_error: 0.6076 - val_loss: 15.3842 - val_mean_absolute_error: 2.9920 Epoch 401/1000 7/7 [==============================] - 0s 16ms/step - loss: 0.7742 - mean_absolute_error: 0.5334 - val_loss: 14.9895 - val_mean_absolute_error: 2.8617 Epoch 402/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.7697 - mean_absolute_error: 0.5132 - val_loss: 15.3199 - val_mean_absolute_error: 2.9568 Epoch 403/1000 7/7 [==============================] - 0s 16ms/step - loss: 0.7158 - mean_absolute_error: 0.4772 - val_loss: 14.5511 - val_mean_absolute_error: 2.9018 Epoch 404/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.6855 - mean_absolute_error: 0.4667 - val_loss: 15.7693 - val_mean_absolute_error: 2.9662 Epoch 405/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.6789 - mean_absolute_error: 0.4597 - val_loss: 14.9693 - val_mean_absolute_error: 2.8708 Epoch 406/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.6920 - mean_absolute_error: 0.4519 - val_loss: 15.3398 - val_mean_absolute_error: 3.0131 Epoch 407/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.6803 - mean_absolute_error: 0.4661 - val_loss: 15.3237 - val_mean_absolute_error: 2.9246 Epoch 408/1000 7/7 [==============================] - 0s 16ms/step - loss: 0.7668 - mean_absolute_error: 0.4443 - val_loss: 15.0203 - val_mean_absolute_error: 2.8517 Epoch 409/1000 7/7 [==============================] - 0s 15ms/step - loss: 1.9748 - mean_absolute_error: 0.5845 - val_loss: 15.9970 - val_mean_absolute_error: 2.9559 Epoch 410/1000 7/7 [==============================] - 0s 16ms/step - loss: 1.2001 - mean_absolute_error: 0.5889 - val_loss: 15.0172 - val_mean_absolute_error: 2.9287 Epoch 411/1000 7/7 [==============================] - 0s 15ms/step - loss: 1.4265 - mean_absolute_error: 0.6976 - val_loss: 14.0423 - val_mean_absolute_error: 2.8386 Epoch 412/1000 7/7 [==============================] - 0s 16ms/step - loss: 1.9102 - mean_absolute_error: 0.8116 - val_loss: 16.7284 - val_mean_absolute_error: 3.0329 Epoch 413/1000 7/7 [==============================] - 0s 15ms/step - loss: 1.3189 - mean_absolute_error: 0.7341 - val_loss: 14.1853 - val_mean_absolute_error: 2.8097 Epoch 414/1000 7/7 [==============================] - 0s 15ms/step - loss: 1.0416 - mean_absolute_error: 0.6753 - val_loss: 15.8384 - val_mean_absolute_error: 3.0647 Epoch 415/1000 7/7 [==============================] - 0s 15ms/step - loss: 1.0588 - mean_absolute_error: 0.6478 - val_loss: 14.6587 - val_mean_absolute_error: 2.8292 Epoch 416/1000 7/7 [==============================] - 0s 16ms/step - loss: 1.3457 - mean_absolute_error: 0.6287 - val_loss: 13.4390 - val_mean_absolute_error: 2.7722 Epoch 417/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.9473 - mean_absolute_error: 0.5921 - val_loss: 15.9665 - val_mean_absolute_error: 3.0619 Epoch 418/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.8768 - mean_absolute_error: 0.5637 - val_loss: 15.7670 - val_mean_absolute_error: 2.9912 Epoch 419/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.9794 - mean_absolute_error: 0.5984 - val_loss: 14.3685 - val_mean_absolute_error: 2.8979 Epoch 420/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.9169 - mean_absolute_error: 0.5412 - val_loss: 16.0598 - val_mean_absolute_error: 2.9864 Epoch 421/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.9223 - mean_absolute_error: 0.5522 - val_loss: 14.3250 - val_mean_absolute_error: 2.8704 Epoch 422/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.6705 - mean_absolute_error: 0.4862 - val_loss: 15.5415 - val_mean_absolute_error: 2.9827 Epoch 423/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.8034 - mean_absolute_error: 0.5151 - val_loss: 15.1364 - val_mean_absolute_error: 2.8985 Epoch 424/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.6107 - mean_absolute_error: 0.4381 - val_loss: 15.4609 - val_mean_absolute_error: 2.9510 Epoch 425/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.7735 - mean_absolute_error: 0.5197 - val_loss: 14.9417 - val_mean_absolute_error: 2.8779 Epoch 426/1000 7/7 [==============================] - 0s 16ms/step - loss: 0.9562 - mean_absolute_error: 0.6041 - val_loss: 15.5725 - val_mean_absolute_error: 2.9210 Epoch 427/1000 7/7 [==============================] - 0s 15ms/step - loss: 1.0616 - mean_absolute_error: 0.5417 - val_loss: 15.0102 - val_mean_absolute_error: 2.9142 Epoch 428/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.8280 - mean_absolute_error: 0.5314 - val_loss: 15.4087 - val_mean_absolute_error: 2.9979 Epoch 429/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.7573 - mean_absolute_error: 0.5404 - val_loss: 15.1866 - val_mean_absolute_error: 2.8714 Epoch 430/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.9358 - mean_absolute_error: 0.6097 - val_loss: 14.9793 - val_mean_absolute_error: 2.9341 Epoch 431/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.7821 - mean_absolute_error: 0.5589 - val_loss: 15.1976 - val_mean_absolute_error: 2.9879 Epoch 432/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.7155 - mean_absolute_error: 0.4600 - val_loss: 16.3737 - val_mean_absolute_error: 3.0219 Epoch 433/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.7998 - mean_absolute_error: 0.5076 - val_loss: 14.9207 - val_mean_absolute_error: 2.9106 Epoch 434/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.6199 - mean_absolute_error: 0.4209 - val_loss: 15.6476 - val_mean_absolute_error: 3.0001 Epoch 435/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.6470 - mean_absolute_error: 0.4256 - val_loss: 15.1753 - val_mean_absolute_error: 2.8351 Epoch 436/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.7205 - mean_absolute_error: 0.4108 - val_loss: 15.8780 - val_mean_absolute_error: 3.0215 Epoch 437/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.6471 - mean_absolute_error: 0.4511 - val_loss: 14.6358 - val_mean_absolute_error: 2.8778 Epoch 438/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.5777 - mean_absolute_error: 0.4180 - val_loss: 15.6385 - val_mean_absolute_error: 2.9318 Epoch 439/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.6925 - mean_absolute_error: 0.4356 - val_loss: 14.8558 - val_mean_absolute_error: 2.9297 Epoch 440/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.8625 - mean_absolute_error: 0.4620 - val_loss: 16.9595 - val_mean_absolute_error: 3.0932 Epoch 441/1000 7/7 [==============================] - 0s 15ms/step - loss: 1.1451 - mean_absolute_error: 0.4683 - val_loss: 14.2107 - val_mean_absolute_error: 2.7148 Epoch 442/1000 7/7 [==============================] - 0s 15ms/step - loss: 2.1124 - mean_absolute_error: 0.6465 - val_loss: 17.6474 - val_mean_absolute_error: 3.2192 Epoch 443/1000 7/7 [==============================] - 0s 15ms/step - loss: 2.2314 - mean_absolute_error: 0.7512 - val_loss: 13.9292 - val_mean_absolute_error: 2.7540 Epoch 444/1000 7/7 [==============================] - 0s 15ms/step - loss: 1.9228 - mean_absolute_error: 0.7925 - val_loss: 16.6153 - val_mean_absolute_error: 3.0886 Epoch 445/1000 7/7 [==============================] - 0s 15ms/step - loss: 1.6260 - mean_absolute_error: 0.8407 - val_loss: 14.5818 - val_mean_absolute_error: 2.9542 Epoch 446/1000 7/7 [==============================] - 0s 15ms/step - loss: 1.5509 - mean_absolute_error: 0.8222 - val_loss: 13.7326 - val_mean_absolute_error: 2.8722 Epoch 447/1000 7/7 [==============================] - 0s 15ms/step - loss: 1.2965 - mean_absolute_error: 0.7491 - val_loss: 17.1325 - val_mean_absolute_error: 3.1158 Epoch 448/1000 7/7 [==============================] - 0s 15ms/step - loss: 1.1965 - mean_absolute_error: 0.7062 - val_loss: 15.2254 - val_mean_absolute_error: 2.8838 Epoch 449/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.9948 - mean_absolute_error: 0.6019 - val_loss: 16.0173 - val_mean_absolute_error: 3.0150 Epoch 450/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.9904 - mean_absolute_error: 0.6090 - val_loss: 15.3145 - val_mean_absolute_error: 2.9215 Epoch 451/1000 7/7 [==============================] - 0s 15ms/step - loss: 1.0100 - mean_absolute_error: 0.6155 - val_loss: 16.3909 - val_mean_absolute_error: 3.0555 Epoch 452/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.9549 - mean_absolute_error: 0.5790 - val_loss: 15.1780 - val_mean_absolute_error: 2.9471 Epoch 453/1000 7/7 [==============================] - 0s 16ms/step - loss: 1.0326 - mean_absolute_error: 0.5882 - val_loss: 16.3885 - val_mean_absolute_error: 2.9892 Epoch 454/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.9114 - mean_absolute_error: 0.5689 - val_loss: 15.0467 - val_mean_absolute_error: 2.8848 Epoch 455/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.7953 - mean_absolute_error: 0.5291 - val_loss: 16.0357 - val_mean_absolute_error: 3.0266 Epoch 456/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.8081 - mean_absolute_error: 0.5223 - val_loss: 16.3689 - val_mean_absolute_error: 2.9534 Epoch 457/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.8731 - mean_absolute_error: 0.5669 - val_loss: 14.2829 - val_mean_absolute_error: 2.8282 Epoch 458/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.8216 - mean_absolute_error: 0.5179 - val_loss: 15.5922 - val_mean_absolute_error: 2.9529 Epoch 459/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.8714 - mean_absolute_error: 0.5749 - val_loss: 15.0020 - val_mean_absolute_error: 2.8101 Epoch 460/1000 7/7 [==============================] - 0s 15ms/step - loss: 1.0676 - mean_absolute_error: 0.5794 - val_loss: 15.5101 - val_mean_absolute_error: 2.9504 Epoch 461/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.7943 - mean_absolute_error: 0.4941 - val_loss: 15.7480 - val_mean_absolute_error: 2.9248 Epoch 462/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.7099 - mean_absolute_error: 0.4637 - val_loss: 15.9321 - val_mean_absolute_error: 2.9363 Epoch 463/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.7399 - mean_absolute_error: 0.4537 - val_loss: 15.0966 - val_mean_absolute_error: 2.8812 Epoch 464/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.5988 - mean_absolute_error: 0.4141 - val_loss: 15.0629 - val_mean_absolute_error: 2.9003 Epoch 465/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.9512 - mean_absolute_error: 0.4819 - val_loss: 16.0684 - val_mean_absolute_error: 2.9688 Epoch 466/1000 7/7 [==============================] - 0s 15ms/step - loss: 2.3406 - mean_absolute_error: 0.8230 - val_loss: 11.3579 - val_mean_absolute_error: 2.6156 Epoch 467/1000 7/7 [==============================] - 0s 15ms/step - loss: 3.1178 - mean_absolute_error: 0.9747 - val_loss: 10.6268 - val_mean_absolute_error: 2.4585 Epoch 468/1000 7/7 [==============================] - 0s 15ms/step - loss: 3.1344 - mean_absolute_error: 1.0361 - val_loss: 12.9022 - val_mean_absolute_error: 2.7432 Epoch 469/1000 7/7 [==============================] - 0s 15ms/step - loss: 2.1830 - mean_absolute_error: 0.9516 - val_loss: 13.9407 - val_mean_absolute_error: 2.8450 Epoch 470/1000 7/7 [==============================] - 0s 15ms/step - loss: 1.9056 - mean_absolute_error: 0.8711 - val_loss: 13.4251 - val_mean_absolute_error: 2.7595 Epoch 471/1000 7/7 [==============================] - 0s 15ms/step - loss: 1.5775 - mean_absolute_error: 0.7880 - val_loss: 13.7000 - val_mean_absolute_error: 2.8687 Epoch 472/1000 7/7 [==============================] - 0s 15ms/step - loss: 1.5196 - mean_absolute_error: 0.7514 - val_loss: 12.8423 - val_mean_absolute_error: 2.7563 Epoch 473/1000 7/7 [==============================] - 0s 15ms/step - loss: 1.2614 - mean_absolute_error: 0.6469 - val_loss: 14.4744 - val_mean_absolute_error: 2.8830 Epoch 474/1000 7/7 [==============================] - 0s 15ms/step - loss: 1.3297 - mean_absolute_error: 0.6471 - val_loss: 13.6753 - val_mean_absolute_error: 2.8258 Epoch 475/1000 7/7 [==============================] - 0s 15ms/step - loss: 1.4173 - mean_absolute_error: 0.6172 - val_loss: 13.8698 - val_mean_absolute_error: 2.8614 Epoch 476/1000 7/7 [==============================] - 0s 15ms/step - loss: 1.5486 - mean_absolute_error: 0.6867 - val_loss: 13.8091 - val_mean_absolute_error: 2.8272 Epoch 477/1000 7/7 [==============================] - 0s 15ms/step - loss: 1.4181 - mean_absolute_error: 0.6256 - val_loss: 13.3685 - val_mean_absolute_error: 2.8311 Epoch 478/1000 7/7 [==============================] - 0s 15ms/step - loss: 1.1772 - mean_absolute_error: 0.5176 - val_loss: 14.4765 - val_mean_absolute_error: 2.9311 Epoch 479/1000 7/7 [==============================] - 0s 15ms/step - loss: 1.1213 - mean_absolute_error: 0.5108 - val_loss: 13.7554 - val_mean_absolute_error: 2.8578 Epoch 480/1000 7/7 [==============================] - 0s 15ms/step - loss: 1.2070 - mean_absolute_error: 0.5320 - val_loss: 13.7372 - val_mean_absolute_error: 2.8270 Epoch 481/1000 7/7 [==============================] - 0s 15ms/step - loss: 1.2335 - mean_absolute_error: 0.5308 - val_loss: 14.5503 - val_mean_absolute_error: 2.8389 Epoch 482/1000 7/7 [==============================] - 0s 15ms/step - loss: 1.1781 - mean_absolute_error: 0.5109 - val_loss: 13.3982 - val_mean_absolute_error: 2.7599 Epoch 483/1000 7/7 [==============================] - 0s 15ms/step - loss: 1.3095 - mean_absolute_error: 0.6084 - val_loss: 14.1845 - val_mean_absolute_error: 2.9351 Epoch 484/1000 7/7 [==============================] - 0s 16ms/step - loss: 1.2240 - mean_absolute_error: 0.5481 - val_loss: 15.0280 - val_mean_absolute_error: 2.9833 Epoch 485/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.9547 - mean_absolute_error: 0.4341 - val_loss: 13.8229 - val_mean_absolute_error: 2.7899 Epoch 486/1000 7/7 [==============================] - 0s 16ms/step - loss: 1.0015 - mean_absolute_error: 0.4841 - val_loss: 14.7414 - val_mean_absolute_error: 2.9261 Epoch 487/1000 7/7 [==============================] - 0s 15ms/step - loss: 1.0110 - mean_absolute_error: 0.4494 - val_loss: 13.9053 - val_mean_absolute_error: 2.8072 Epoch 488/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.9528 - mean_absolute_error: 0.4019 - val_loss: 14.1300 - val_mean_absolute_error: 2.8217 Epoch 489/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.9068 - mean_absolute_error: 0.4038 - val_loss: 14.7281 - val_mean_absolute_error: 2.9123 Epoch 490/1000 7/7 [==============================] - 0s 16ms/step - loss: 0.9270 - mean_absolute_error: 0.4164 - val_loss: 14.0789 - val_mean_absolute_error: 2.8263 Epoch 491/1000 7/7 [==============================] - 0s 15ms/step - loss: 1.0017 - mean_absolute_error: 0.4627 - val_loss: 14.7807 - val_mean_absolute_error: 2.8778 Epoch 492/1000 7/7 [==============================] - 0s 15ms/step - loss: 1.1121 - mean_absolute_error: 0.5299 - val_loss: 14.3583 - val_mean_absolute_error: 2.8710 Epoch 493/1000 7/7 [==============================] - 0s 15ms/step - loss: 1.1945 - mean_absolute_error: 0.5431 - val_loss: 14.0176 - val_mean_absolute_error: 2.8141 Epoch 494/1000 7/7 [==============================] - 0s 15ms/step - loss: 1.0628 - mean_absolute_error: 0.5261 - val_loss: 14.7809 - val_mean_absolute_error: 2.8787 Epoch 495/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.9868 - mean_absolute_error: 0.5054 - val_loss: 14.7605 - val_mean_absolute_error: 2.8785 Epoch 496/1000 7/7 [==============================] - 0s 16ms/step - loss: 0.8914 - mean_absolute_error: 0.4682 - val_loss: 14.7949 - val_mean_absolute_error: 2.8371 Epoch 497/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.8569 - mean_absolute_error: 0.4468 - val_loss: 14.5394 - val_mean_absolute_error: 2.8935 Epoch 498/1000 7/7 [==============================] - 0s 16ms/step - loss: 0.8549 - mean_absolute_error: 0.4274 - val_loss: 14.6594 - val_mean_absolute_error: 2.8712 Epoch 499/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.7901 - mean_absolute_error: 0.4181 - val_loss: 14.9536 - val_mean_absolute_error: 2.9095 Epoch 500/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.7626 - mean_absolute_error: 0.3807 - val_loss: 14.7007 - val_mean_absolute_error: 2.8559 Epoch 501/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.7584 - mean_absolute_error: 0.3879 - val_loss: 14.6191 - val_mean_absolute_error: 2.8159 Epoch 502/1000 7/7 [==============================] - 0s 16ms/step - loss: 0.7273 - mean_absolute_error: 0.3806 - val_loss: 14.7351 - val_mean_absolute_error: 2.8804 Epoch 503/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.8014 - mean_absolute_error: 0.3901 - val_loss: 14.3114 - val_mean_absolute_error: 2.8300 Epoch 504/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.7084 - mean_absolute_error: 0.3923 - val_loss: 14.9840 - val_mean_absolute_error: 2.8830 Epoch 505/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.6986 - mean_absolute_error: 0.3593 - val_loss: 14.5852 - val_mean_absolute_error: 2.8709 Epoch 506/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.6406 - mean_absolute_error: 0.3416 - val_loss: 14.9583 - val_mean_absolute_error: 2.9237 Epoch 507/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.6541 - mean_absolute_error: 0.3718 - val_loss: 14.9956 - val_mean_absolute_error: 2.9042 Epoch 508/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.6620 - mean_absolute_error: 0.3834 - val_loss: 14.5334 - val_mean_absolute_error: 2.8597 Epoch 509/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.7298 - mean_absolute_error: 0.4103 - val_loss: 14.5337 - val_mean_absolute_error: 2.8548 Epoch 510/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.6670 - mean_absolute_error: 0.3959 - val_loss: 15.6344 - val_mean_absolute_error: 2.9670 Epoch 511/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.8940 - mean_absolute_error: 0.4587 - val_loss: 14.8996 - val_mean_absolute_error: 2.8700 Epoch 512/1000 7/7 [==============================] - 0s 24ms/step - loss: 1.2976 - mean_absolute_error: 0.5015 - val_loss: 15.7204 - val_mean_absolute_error: 2.9513 Epoch 513/1000 7/7 [==============================] - 0s 16ms/step - loss: 1.4781 - mean_absolute_error: 0.4885 - val_loss: 15.0092 - val_mean_absolute_error: 2.8903 Epoch 514/1000 7/7 [==============================] - 0s 22ms/step - loss: 1.0398 - mean_absolute_error: 0.5481 - val_loss: 16.0997 - val_mean_absolute_error: 3.1061 Epoch 515/1000 7/7 [==============================] - 0s 16ms/step - loss: 1.0434 - mean_absolute_error: 0.5729 - val_loss: 14.9528 - val_mean_absolute_error: 2.8786 Epoch 516/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.7856 - mean_absolute_error: 0.4679 - val_loss: 15.5633 - val_mean_absolute_error: 2.9829 Epoch 517/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.0920 - mean_absolute_error: 0.5818 - val_loss: 14.9440 - val_mean_absolute_error: 2.9014 Epoch 518/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.0739 - mean_absolute_error: 0.5398 - val_loss: 15.1940 - val_mean_absolute_error: 2.9487 Epoch 519/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.9460 - mean_absolute_error: 0.5242 - val_loss: 14.1632 - val_mean_absolute_error: 2.8628 Epoch 520/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.9734 - mean_absolute_error: 0.6007 - val_loss: 15.3601 - val_mean_absolute_error: 2.9674 Epoch 521/1000 7/7 [==============================] - 0s 19ms/step - loss: 1.0335 - mean_absolute_error: 0.6215 - val_loss: 14.7830 - val_mean_absolute_error: 2.9033 Epoch 522/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.7897 - mean_absolute_error: 0.5337 - val_loss: 14.9846 - val_mean_absolute_error: 2.9005 Epoch 523/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.7990 - mean_absolute_error: 0.5478 - val_loss: 15.2219 - val_mean_absolute_error: 2.9137 Epoch 524/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.7687 - mean_absolute_error: 0.5376 - val_loss: 15.2334 - val_mean_absolute_error: 2.9504 Epoch 525/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.7368 - mean_absolute_error: 0.4703 - val_loss: 15.4261 - val_mean_absolute_error: 2.9754 Epoch 526/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.7711 - mean_absolute_error: 0.4838 - val_loss: 14.3329 - val_mean_absolute_error: 2.8441 Epoch 527/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.6489 - mean_absolute_error: 0.4207 - val_loss: 15.9444 - val_mean_absolute_error: 2.9881 Epoch 528/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.6497 - mean_absolute_error: 0.4528 - val_loss: 14.6559 - val_mean_absolute_error: 2.8803 Epoch 529/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.5628 - mean_absolute_error: 0.3903 - val_loss: 15.5840 - val_mean_absolute_error: 2.9807 Epoch 530/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.5026 - mean_absolute_error: 0.3407 - val_loss: 15.6675 - val_mean_absolute_error: 2.9721 Epoch 531/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.4840 - mean_absolute_error: 0.2928 - val_loss: 15.2509 - val_mean_absolute_error: 2.9541 Epoch 532/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.5484 - mean_absolute_error: 0.3319 - val_loss: 15.6579 - val_mean_absolute_error: 2.9639 Epoch 533/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.5740 - mean_absolute_error: 0.3693 - val_loss: 15.2545 - val_mean_absolute_error: 2.9246 Epoch 534/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.6114 - mean_absolute_error: 0.4250 - val_loss: 15.2363 - val_mean_absolute_error: 2.9638 Epoch 535/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.9002 - mean_absolute_error: 0.4356 - val_loss: 16.1343 - val_mean_absolute_error: 3.0527 Epoch 536/1000 7/7 [==============================] - 0s 15ms/step - loss: 1.9724 - mean_absolute_error: 0.5448 - val_loss: 14.6286 - val_mean_absolute_error: 2.8547 Epoch 537/1000 7/7 [==============================] - 0s 15ms/step - loss: 1.9998 - mean_absolute_error: 0.8126 - val_loss: 17.1937 - val_mean_absolute_error: 3.1362 Epoch 538/1000 7/7 [==============================] - 0s 15ms/step - loss: 2.1139 - mean_absolute_error: 0.9005 - val_loss: 14.5021 - val_mean_absolute_error: 2.8689 Epoch 539/1000 7/7 [==============================] - 0s 15ms/step - loss: 1.5906 - mean_absolute_error: 0.7645 - val_loss: 16.7740 - val_mean_absolute_error: 3.0583 Epoch 540/1000 7/7 [==============================] - 0s 15ms/step - loss: 1.8182 - mean_absolute_error: 0.8288 - val_loss: 16.4494 - val_mean_absolute_error: 3.1343 Epoch 541/1000 7/7 [==============================] - 0s 15ms/step - loss: 1.7716 - mean_absolute_error: 0.8054 - val_loss: 15.8010 - val_mean_absolute_error: 2.9933 Epoch 542/1000 7/7 [==============================] - 0s 33ms/step - loss: 2.4020 - mean_absolute_error: 0.8970 - val_loss: 15.8894 - val_mean_absolute_error: 2.9666 Epoch 543/1000 7/7 [==============================] - 0s 43ms/step - loss: 2.0767 - mean_absolute_error: 0.8572 - val_loss: 16.2573 - val_mean_absolute_error: 3.0049 Epoch 544/1000 7/7 [==============================] - 0s 14ms/step - loss: 1.6128 - mean_absolute_error: 0.7692 - val_loss: 16.0778 - val_mean_absolute_error: 2.9832 Epoch 545/1000 7/7 [==============================] - 0s 14ms/step - loss: 1.4733 - mean_absolute_error: 0.6698 - val_loss: 15.9223 - val_mean_absolute_error: 3.0190 Epoch 546/1000 7/7 [==============================] - 0s 14ms/step - loss: 1.0951 - mean_absolute_error: 0.5880 - val_loss: 14.9046 - val_mean_absolute_error: 2.8850 Epoch 547/1000 7/7 [==============================] - 618s 103s/step - loss: 0.9977 - mean_absolute_error: 0.5474 - val_loss: 16.0732 - val_mean_absolute_error: 3.0123 Epoch 548/1000 7/7 [==============================] - 1s 108ms/step - loss: 0.9274 - mean_absolute_error: 0.4987 - val_loss: 15.5388 - val_mean_absolute_error: 2.9776 Epoch 549/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.8951 - mean_absolute_error: 0.4280 - val_loss: 15.4364 - val_mean_absolute_error: 2.9251 Epoch 550/1000 7/7 [==============================] - 1s 203ms/step - loss: 0.8296 - mean_absolute_error: 0.4183 - val_loss: 15.9844 - val_mean_absolute_error: 3.0083 Epoch 551/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.7458 - mean_absolute_error: 0.4062 - val_loss: 15.5481 - val_mean_absolute_error: 2.9698 Epoch 552/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.7969 - mean_absolute_error: 0.4224 - val_loss: 15.9077 - val_mean_absolute_error: 3.0126 Epoch 553/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.7677 - mean_absolute_error: 0.4298 - val_loss: 15.5542 - val_mean_absolute_error: 2.9229 Epoch 554/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.8016 - mean_absolute_error: 0.4288 - val_loss: 16.1278 - val_mean_absolute_error: 3.0028 Epoch 555/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.7462 - mean_absolute_error: 0.4071 - val_loss: 15.7517 - val_mean_absolute_error: 3.0610 Epoch 556/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.8859 - mean_absolute_error: 0.4986 - val_loss: 16.6097 - val_mean_absolute_error: 3.0612 Epoch 557/1000 7/7 [==============================] - 0s 26ms/step - loss: 1.3332 - mean_absolute_error: 0.5424 - val_loss: 15.7544 - val_mean_absolute_error: 3.0092 Epoch 558/1000 7/7 [==============================] - 0s 30ms/step - loss: 1.2640 - mean_absolute_error: 0.5320 - val_loss: 16.1901 - val_mean_absolute_error: 3.0662 Epoch 559/1000 7/7 [==============================] - 0s 26ms/step - loss: 1.5046 - mean_absolute_error: 0.5573 - val_loss: 15.8182 - val_mean_absolute_error: 3.0281 Epoch 560/1000 7/7 [==============================] - 0s 36ms/step - loss: 1.4201 - mean_absolute_error: 0.5693 - val_loss: 15.5529 - val_mean_absolute_error: 2.9637 Epoch 561/1000 7/7 [==============================] - 0s 29ms/step - loss: 1.4132 - mean_absolute_error: 0.7036 - val_loss: 16.2963 - val_mean_absolute_error: 3.0333 Epoch 562/1000 7/7 [==============================] - 0s 31ms/step - loss: 1.5316 - mean_absolute_error: 0.8015 - val_loss: 16.0599 - val_mean_absolute_error: 3.0396 Epoch 563/1000 7/7 [==============================] - 0s 47ms/step - loss: 1.3171 - mean_absolute_error: 0.7551 - val_loss: 15.5025 - val_mean_absolute_error: 2.9735 Epoch 564/1000 7/7 [==============================] - 0s 29ms/step - loss: 1.0798 - mean_absolute_error: 0.6599 - val_loss: 15.9672 - val_mean_absolute_error: 2.9780 Epoch 565/1000 7/7 [==============================] - 0s 20ms/step - loss: 1.0870 - mean_absolute_error: 0.6303 - val_loss: 18.6982 - val_mean_absolute_error: 3.2819 Epoch 566/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.9594 - mean_absolute_error: 0.6023 - val_loss: 15.8502 - val_mean_absolute_error: 3.0427 Epoch 567/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.7807 - mean_absolute_error: 0.5000 - val_loss: 16.4300 - val_mean_absolute_error: 3.1396 Epoch 568/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.7806 - mean_absolute_error: 0.4841 - val_loss: 15.3018 - val_mean_absolute_error: 2.9938 Epoch 569/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.6653 - mean_absolute_error: 0.4604 - val_loss: 16.5786 - val_mean_absolute_error: 3.1211 Epoch 570/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.8334 - mean_absolute_error: 0.4191 - val_loss: 15.8170 - val_mean_absolute_error: 2.9828 Epoch 571/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.7227 - mean_absolute_error: 0.3878 - val_loss: 15.8574 - val_mean_absolute_error: 3.1006 Epoch 572/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.8153 - mean_absolute_error: 0.5113 - val_loss: 15.3862 - val_mean_absolute_error: 2.9438 Epoch 573/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.8276 - mean_absolute_error: 0.4781 - val_loss: 15.6836 - val_mean_absolute_error: 2.9452 Epoch 574/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.7297 - mean_absolute_error: 0.4022 - val_loss: 15.3797 - val_mean_absolute_error: 2.9841 Epoch 575/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.8463 - mean_absolute_error: 0.4702 - val_loss: 16.7636 - val_mean_absolute_error: 3.0862 Epoch 576/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.6406 - mean_absolute_error: 0.3950 - val_loss: 15.5958 - val_mean_absolute_error: 2.9846 Epoch 577/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.6138 - mean_absolute_error: 0.3967 - val_loss: 15.8606 - val_mean_absolute_error: 3.0191 Epoch 578/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.5040 - mean_absolute_error: 0.3570 - val_loss: 16.1317 - val_mean_absolute_error: 3.0211 Epoch 579/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.5338 - mean_absolute_error: 0.3505 - val_loss: 16.1431 - val_mean_absolute_error: 3.0029 Epoch 580/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.4561 - mean_absolute_error: 0.3248 - val_loss: 16.4190 - val_mean_absolute_error: 3.0436 Epoch 581/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.6004 - mean_absolute_error: 0.3628 - val_loss: 15.8804 - val_mean_absolute_error: 2.9739 Epoch 582/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.5612 - mean_absolute_error: 0.4046 - val_loss: 16.5089 - val_mean_absolute_error: 3.0548 Epoch 583/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.4845 - mean_absolute_error: 0.3530 - val_loss: 15.7578 - val_mean_absolute_error: 3.0459 Epoch 584/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.4828 - mean_absolute_error: 0.3372 - val_loss: 16.2087 - val_mean_absolute_error: 3.0055 Epoch 585/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.4999 - mean_absolute_error: 0.4299 - val_loss: 15.7633 - val_mean_absolute_error: 2.9277 Epoch 586/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.8807 - mean_absolute_error: 0.4309 - val_loss: 16.7139 - val_mean_absolute_error: 3.0945 Epoch 587/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.8684 - mean_absolute_error: 0.4664 - val_loss: 16.0883 - val_mean_absolute_error: 2.9923 Epoch 588/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.7478 - mean_absolute_error: 0.4966 - val_loss: 16.1496 - val_mean_absolute_error: 3.0287 Epoch 589/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.6019 - mean_absolute_error: 0.4512 - val_loss: 15.3061 - val_mean_absolute_error: 2.9048 Epoch 590/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.6059 - mean_absolute_error: 0.3927 - val_loss: 16.6921 - val_mean_absolute_error: 3.1427 Epoch 591/1000 7/7 [==============================] - 0s 57ms/step - loss: 0.6416 - mean_absolute_error: 0.4052 - val_loss: 15.4564 - val_mean_absolute_error: 2.9845 Epoch 592/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.5132 - mean_absolute_error: 0.3344 - val_loss: 16.4897 - val_mean_absolute_error: 3.0745 Epoch 593/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.4940 - mean_absolute_error: 0.3442 - val_loss: 15.1556 - val_mean_absolute_error: 2.8639 Epoch 594/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.4860 - mean_absolute_error: 0.3514 - val_loss: 16.8204 - val_mean_absolute_error: 3.0768 Epoch 595/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.4559 - mean_absolute_error: 0.3449 - val_loss: 15.3996 - val_mean_absolute_error: 2.9399 Epoch 596/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.4759 - mean_absolute_error: 0.3505 - val_loss: 16.1137 - val_mean_absolute_error: 3.0020 Epoch 597/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.4496 - mean_absolute_error: 0.3296 - val_loss: 15.9588 - val_mean_absolute_error: 2.9829 Epoch 598/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.4274 - mean_absolute_error: 0.2972 - val_loss: 16.4622 - val_mean_absolute_error: 3.0562 Epoch 599/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.4253 - mean_absolute_error: 0.2805 - val_loss: 16.0073 - val_mean_absolute_error: 3.0287 Epoch 600/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.4017 - mean_absolute_error: 0.3157 - val_loss: 16.1823 - val_mean_absolute_error: 3.0111 Epoch 601/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.7089 - mean_absolute_error: 0.3471 - val_loss: 16.5762 - val_mean_absolute_error: 3.0382 Epoch 602/1000 7/7 [==============================] - 0s 40ms/step - loss: 0.6398 - mean_absolute_error: 0.3344 - val_loss: 15.4044 - val_mean_absolute_error: 2.9242 Epoch 603/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.5287 - mean_absolute_error: 0.3398 - val_loss: 16.0395 - val_mean_absolute_error: 3.0294 Epoch 604/1000 7/7 [==============================] - 0s 53ms/step - loss: 0.5809 - mean_absolute_error: 0.3350 - val_loss: 15.8405 - val_mean_absolute_error: 2.9516 Epoch 605/1000 7/7 [==============================] - 0s 43ms/step - loss: 0.5109 - mean_absolute_error: 0.3259 - val_loss: 15.9867 - val_mean_absolute_error: 3.0491 Epoch 606/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.5813 - mean_absolute_error: 0.3581 - val_loss: 16.0572 - val_mean_absolute_error: 2.9736 Epoch 607/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.5113 - mean_absolute_error: 0.3245 - val_loss: 16.5362 - val_mean_absolute_error: 3.0316 Epoch 608/1000 7/7 [==============================] - 0s 40ms/step - loss: 0.4953 - mean_absolute_error: 0.2910 - val_loss: 16.3770 - val_mean_absolute_error: 2.9940 Epoch 609/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.4435 - mean_absolute_error: 0.3160 - val_loss: 15.6112 - val_mean_absolute_error: 2.9316 Epoch 610/1000 7/7 [==============================] - 0s 46ms/step - loss: 0.3992 - mean_absolute_error: 0.2969 - val_loss: 16.3772 - val_mean_absolute_error: 3.0149 Epoch 611/1000 7/7 [==============================] - 0s 59ms/step - loss: 0.4530 - mean_absolute_error: 0.2966 - val_loss: 16.1207 - val_mean_absolute_error: 2.9927 Epoch 612/1000 7/7 [==============================] - 0s 44ms/step - loss: 0.3642 - mean_absolute_error: 0.2787 - val_loss: 16.0564 - val_mean_absolute_error: 3.0394 Epoch 613/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.4239 - mean_absolute_error: 0.3019 - val_loss: 15.5188 - val_mean_absolute_error: 2.8891 Epoch 614/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.4084 - mean_absolute_error: 0.3037 - val_loss: 16.7355 - val_mean_absolute_error: 3.0682 Epoch 615/1000 7/7 [==============================] - 0s 40ms/step - loss: 0.3818 - mean_absolute_error: 0.2884 - val_loss: 15.8237 - val_mean_absolute_error: 2.9746 Epoch 616/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.3407 - mean_absolute_error: 0.2636 - val_loss: 17.0304 - val_mean_absolute_error: 3.1065 Epoch 617/1000 7/7 [==============================] - 0s 65ms/step - loss: 0.4033 - mean_absolute_error: 0.3022 - val_loss: 15.6846 - val_mean_absolute_error: 2.9389 Epoch 618/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.3377 - mean_absolute_error: 0.2727 - val_loss: 16.9566 - val_mean_absolute_error: 3.0488 Epoch 619/1000 7/7 [==============================] - 0s 44ms/step - loss: 0.3607 - mean_absolute_error: 0.2812 - val_loss: 16.0550 - val_mean_absolute_error: 2.9667 Epoch 620/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.3030 - mean_absolute_error: 0.2738 - val_loss: 16.2687 - val_mean_absolute_error: 3.0193 Epoch 621/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.3062 - mean_absolute_error: 0.2782 - val_loss: 16.6478 - val_mean_absolute_error: 3.0556 Epoch 622/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.3541 - mean_absolute_error: 0.2668 - val_loss: 15.3291 - val_mean_absolute_error: 2.9072 Epoch 623/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.3166 - mean_absolute_error: 0.2521 - val_loss: 16.5580 - val_mean_absolute_error: 3.0364 Epoch 624/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.3590 - mean_absolute_error: 0.2633 - val_loss: 15.7802 - val_mean_absolute_error: 2.9623 Epoch 625/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.3977 - mean_absolute_error: 0.2979 - val_loss: 16.5705 - val_mean_absolute_error: 3.0679 Epoch 626/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.3142 - mean_absolute_error: 0.2991 - val_loss: 15.7088 - val_mean_absolute_error: 2.8935 Epoch 627/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.4124 - mean_absolute_error: 0.2898 - val_loss: 16.9821 - val_mean_absolute_error: 3.1216 Epoch 628/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.3223 - mean_absolute_error: 0.3083 - val_loss: 15.3845 - val_mean_absolute_error: 2.8778 Epoch 629/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.4059 - mean_absolute_error: 0.3120 - val_loss: 17.5601 - val_mean_absolute_error: 3.1572 Epoch 630/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.3255 - mean_absolute_error: 0.3546 - val_loss: 15.7412 - val_mean_absolute_error: 2.9061 Epoch 631/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.4521 - mean_absolute_error: 0.3216 - val_loss: 16.2159 - val_mean_absolute_error: 3.0163 Epoch 632/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.2716 - mean_absolute_error: 0.2883 - val_loss: 15.9006 - val_mean_absolute_error: 2.9695 Epoch 633/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.3531 - mean_absolute_error: 0.3333 - val_loss: 16.8412 - val_mean_absolute_error: 3.1000 Epoch 634/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.3808 - mean_absolute_error: 0.3559 - val_loss: 15.5807 - val_mean_absolute_error: 2.9173 Epoch 635/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.3634 - mean_absolute_error: 0.3496 - val_loss: 17.3291 - val_mean_absolute_error: 3.1329 Epoch 636/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.2999 - mean_absolute_error: 0.3140 - val_loss: 16.1558 - val_mean_absolute_error: 2.9760 Epoch 637/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.4086 - mean_absolute_error: 0.3307 - val_loss: 16.3812 - val_mean_absolute_error: 3.0174 Epoch 638/1000 7/7 [==============================] - 0s 41ms/step - loss: 1.6160 - mean_absolute_error: 0.6463 - val_loss: 16.0674 - val_mean_absolute_error: 2.9836 Epoch 639/1000 7/7 [==============================] - 0s 33ms/step - loss: 1.5610 - mean_absolute_error: 0.6488 - val_loss: 15.8495 - val_mean_absolute_error: 2.8730 Epoch 640/1000 7/7 [==============================] - 0s 31ms/step - loss: 1.1100 - mean_absolute_error: 0.6065 - val_loss: 17.3564 - val_mean_absolute_error: 3.1699 Epoch 641/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.8995 - mean_absolute_error: 0.5622 - val_loss: 15.7215 - val_mean_absolute_error: 3.0196 Epoch 642/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.7371 - mean_absolute_error: 0.4756 - val_loss: 16.4503 - val_mean_absolute_error: 3.0555 Epoch 643/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.6038 - mean_absolute_error: 0.3924 - val_loss: 16.9357 - val_mean_absolute_error: 3.0849 Epoch 644/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.5402 - mean_absolute_error: 0.3553 - val_loss: 16.5539 - val_mean_absolute_error: 3.0414 Epoch 645/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.5282 - mean_absolute_error: 0.3291 - val_loss: 16.8044 - val_mean_absolute_error: 3.0748 Epoch 646/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.4732 - mean_absolute_error: 0.2978 - val_loss: 16.6528 - val_mean_absolute_error: 3.0364 Epoch 647/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.4335 - mean_absolute_error: 0.2605 - val_loss: 16.3954 - val_mean_absolute_error: 3.0577 Epoch 648/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.4560 - mean_absolute_error: 0.2878 - val_loss: 16.2824 - val_mean_absolute_error: 2.9983 Epoch 649/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.4173 - mean_absolute_error: 0.2506 - val_loss: 17.1517 - val_mean_absolute_error: 3.0843 Epoch 650/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.4159 - mean_absolute_error: 0.2389 - val_loss: 16.1132 - val_mean_absolute_error: 2.9926 Epoch 651/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.5582 - mean_absolute_error: 0.2832 - val_loss: 17.4520 - 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mean_absolute_error: 0.3721 - val_loss: 16.5415 - val_mean_absolute_error: 3.0416 Epoch 671/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.4922 - mean_absolute_error: 0.3742 - val_loss: 17.4225 - val_mean_absolute_error: 3.1358 Epoch 672/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.5334 - mean_absolute_error: 0.3677 - val_loss: 16.4477 - val_mean_absolute_error: 3.0559 Epoch 673/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.6448 - mean_absolute_error: 0.3904 - val_loss: 16.6915 - val_mean_absolute_error: 3.0646 Epoch 674/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.5804 - mean_absolute_error: 0.3626 - val_loss: 16.9705 - val_mean_absolute_error: 3.1537 Epoch 675/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.6011 - mean_absolute_error: 0.3307 - val_loss: 16.1024 - val_mean_absolute_error: 2.9649 Epoch 676/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.5314 - mean_absolute_error: 0.3238 - val_loss: 16.9740 - val_mean_absolute_error: 3.0390 Epoch 677/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.5736 - mean_absolute_error: 0.3520 - val_loss: 15.4736 - val_mean_absolute_error: 2.9187 Epoch 678/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.5255 - mean_absolute_error: 0.3680 - val_loss: 16.8157 - val_mean_absolute_error: 3.0596 Epoch 679/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.5041 - mean_absolute_error: 0.3776 - val_loss: 15.9767 - val_mean_absolute_error: 3.0014 Epoch 680/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.5133 - mean_absolute_error: 0.3468 - val_loss: 17.5915 - val_mean_absolute_error: 3.1546 Epoch 681/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.4839 - mean_absolute_error: 0.3486 - val_loss: 15.6944 - val_mean_absolute_error: 2.9328 Epoch 682/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.4829 - mean_absolute_error: 0.3236 - val_loss: 17.2338 - 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loss: 0.5785 - mean_absolute_error: 0.3521 - val_loss: 16.8352 - val_mean_absolute_error: 3.1122 Epoch 696/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.9916 - mean_absolute_error: 0.5147 - val_loss: 15.7323 - val_mean_absolute_error: 2.9668 Epoch 697/1000 7/7 [==============================] - 0s 37ms/step - loss: 1.8379 - mean_absolute_error: 0.6905 - val_loss: 17.6971 - val_mean_absolute_error: 3.1176 Epoch 698/1000 7/7 [==============================] - 0s 26ms/step - loss: 1.6802 - mean_absolute_error: 0.6714 - val_loss: 16.3004 - val_mean_absolute_error: 3.0332 Epoch 699/1000 7/7 [==============================] - 0s 25ms/step - loss: 1.3780 - mean_absolute_error: 0.6129 - val_loss: 17.1458 - val_mean_absolute_error: 3.1155 Epoch 700/1000 7/7 [==============================] - 0s 28ms/step - loss: 1.2163 - mean_absolute_error: 0.5697 - val_loss: 16.4724 - val_mean_absolute_error: 3.0703 Epoch 701/1000 7/7 [==============================] - 0s 29ms/step - loss: 1.1960 - mean_absolute_error: 0.5690 - val_loss: 16.3488 - val_mean_absolute_error: 3.0343 Epoch 702/1000 7/7 [==============================] - 0s 33ms/step - loss: 1.0001 - mean_absolute_error: 0.4524 - val_loss: 16.1202 - val_mean_absolute_error: 3.0201 Epoch 703/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.9763 - mean_absolute_error: 0.4644 - val_loss: 16.7114 - val_mean_absolute_error: 3.0874 Epoch 704/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.9808 - mean_absolute_error: 0.4071 - val_loss: 16.6788 - val_mean_absolute_error: 3.0660 Epoch 705/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.9395 - mean_absolute_error: 0.3874 - val_loss: 15.9799 - val_mean_absolute_error: 3.0260 Epoch 706/1000 7/7 [==============================] - 0s 31ms/step - loss: 1.3466 - mean_absolute_error: 0.5218 - val_loss: 16.2881 - val_mean_absolute_error: 3.0621 Epoch 707/1000 7/7 [==============================] - 0s 29ms/step - loss: 1.2369 - mean_absolute_error: 0.5281 - val_loss: 16.9393 - val_mean_absolute_error: 3.0900 Epoch 708/1000 7/7 [==============================] - 0s 32ms/step - loss: 1.2027 - mean_absolute_error: 0.5122 - val_loss: 15.8885 - val_mean_absolute_error: 2.9759 Epoch 709/1000 7/7 [==============================] - 0s 33ms/step - loss: 1.1112 - mean_absolute_error: 0.4516 - val_loss: 16.2586 - val_mean_absolute_error: 2.9748 Epoch 710/1000 7/7 [==============================] - 0s 28ms/step - loss: 1.0514 - mean_absolute_error: 0.4494 - val_loss: 15.4357 - val_mean_absolute_error: 2.9397 Epoch 711/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.9812 - mean_absolute_error: 0.4155 - val_loss: 16.4119 - val_mean_absolute_error: 3.0413 Epoch 712/1000 7/7 [==============================] - 0s 28ms/step - loss: 1.0546 - mean_absolute_error: 0.4405 - val_loss: 16.8157 - val_mean_absolute_error: 3.0468 Epoch 713/1000 7/7 [==============================] - 0s 26ms/step - loss: 1.0229 - mean_absolute_error: 0.4795 - val_loss: 16.2342 - val_mean_absolute_error: 3.0161 Epoch 714/1000 7/7 [==============================] - 0s 25ms/step - loss: 1.0045 - mean_absolute_error: 0.4937 - val_loss: 16.0713 - val_mean_absolute_error: 2.9822 Epoch 715/1000 7/7 [==============================] - 0s 28ms/step - loss: 1.0539 - mean_absolute_error: 0.5074 - val_loss: 16.3334 - val_mean_absolute_error: 3.0078 Epoch 716/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.9835 - mean_absolute_error: 0.4843 - val_loss: 16.7441 - val_mean_absolute_error: 3.0437 Epoch 717/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.8583 - mean_absolute_error: 0.4418 - val_loss: 16.1023 - val_mean_absolute_error: 3.0517 Epoch 718/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.7817 - mean_absolute_error: 0.3921 - val_loss: 16.8249 - val_mean_absolute_error: 3.0787 Epoch 719/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.8434 - mean_absolute_error: 0.3788 - val_loss: 15.9378 - val_mean_absolute_error: 2.9595 Epoch 720/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.8194 - mean_absolute_error: 0.3960 - val_loss: 16.5396 - val_mean_absolute_error: 3.0612 Epoch 721/1000 7/7 [==============================] - 0s 28ms/step - loss: 1.1333 - mean_absolute_error: 0.4715 - val_loss: 16.3009 - val_mean_absolute_error: 3.0332 Epoch 722/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.9665 - mean_absolute_error: 0.4364 - val_loss: 16.5211 - val_mean_absolute_error: 3.0246 Epoch 723/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.7545 - mean_absolute_error: 0.4002 - val_loss: 16.5257 - val_mean_absolute_error: 3.0138 Epoch 724/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.8540 - mean_absolute_error: 0.3838 - val_loss: 15.7953 - val_mean_absolute_error: 2.9862 Epoch 725/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.7617 - mean_absolute_error: 0.4367 - val_loss: 16.7357 - val_mean_absolute_error: 3.0778 Epoch 726/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.6886 - mean_absolute_error: 0.4044 - val_loss: 15.6004 - val_mean_absolute_error: 2.9028 Epoch 727/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.7601 - mean_absolute_error: 0.4165 - val_loss: 16.8996 - val_mean_absolute_error: 3.1201 Epoch 728/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.7503 - mean_absolute_error: 0.4878 - val_loss: 16.3479 - val_mean_absolute_error: 3.0148 Epoch 729/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.9064 - mean_absolute_error: 0.4919 - val_loss: 16.3341 - val_mean_absolute_error: 3.0204 Epoch 730/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.6362 - mean_absolute_error: 0.4194 - val_loss: 16.7590 - val_mean_absolute_error: 3.0587 Epoch 731/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.7206 - mean_absolute_error: 0.4341 - val_loss: 16.5349 - val_mean_absolute_error: 3.0610 Epoch 732/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.6255 - mean_absolute_error: 0.4262 - val_loss: 16.8572 - val_mean_absolute_error: 3.0892 Epoch 733/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.6980 - mean_absolute_error: 0.3635 - val_loss: 16.5642 - val_mean_absolute_error: 3.0812 Epoch 734/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.8403 - mean_absolute_error: 0.4411 - val_loss: 15.9799 - val_mean_absolute_error: 2.9984 Epoch 735/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.5277 - mean_absolute_error: 0.3955 - val_loss: 16.4303 - val_mean_absolute_error: 3.0200 Epoch 736/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.7420 - mean_absolute_error: 0.4320 - val_loss: 16.9356 - val_mean_absolute_error: 3.1351 Epoch 737/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.6842 - mean_absolute_error: 0.3832 - val_loss: 16.2663 - val_mean_absolute_error: 3.0252 Epoch 738/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.7802 - mean_absolute_error: 0.4358 - val_loss: 16.9242 - val_mean_absolute_error: 3.1173 Epoch 739/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.6248 - mean_absolute_error: 0.4406 - val_loss: 16.7169 - val_mean_absolute_error: 3.0534 Epoch 740/1000 7/7 [==============================] - 0s 29ms/step - loss: 1.1793 - mean_absolute_error: 0.4999 - val_loss: 16.7809 - val_mean_absolute_error: 3.0601 Epoch 741/1000 7/7 [==============================] - 0s 29ms/step - loss: 1.0582 - mean_absolute_error: 0.5440 - val_loss: 15.6572 - val_mean_absolute_error: 2.9560 Epoch 742/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.6995 - mean_absolute_error: 0.4980 - val_loss: 16.6438 - val_mean_absolute_error: 3.0480 Epoch 743/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.6461 - mean_absolute_error: 0.4275 - val_loss: 16.4059 - val_mean_absolute_error: 3.0453 Epoch 744/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.5607 - mean_absolute_error: 0.3982 - val_loss: 16.0766 - val_mean_absolute_error: 2.9941 Epoch 745/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.7184 - mean_absolute_error: 0.4043 - val_loss: 17.2901 - val_mean_absolute_error: 3.1076 Epoch 746/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.9491 - mean_absolute_error: 0.4448 - val_loss: 14.6074 - val_mean_absolute_error: 2.9192 Epoch 747/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.9472 - mean_absolute_error: 0.5171 - val_loss: 18.0172 - val_mean_absolute_error: 3.1863 Epoch 748/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.6694 - mean_absolute_error: 0.4453 - val_loss: 15.0289 - val_mean_absolute_error: 2.9294 Epoch 749/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.6672 - mean_absolute_error: 0.4583 - val_loss: 18.2421 - val_mean_absolute_error: 3.1955 Epoch 750/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.5703 - mean_absolute_error: 0.4230 - val_loss: 14.9707 - val_mean_absolute_error: 2.8692 Epoch 751/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.6763 - mean_absolute_error: 0.3985 - val_loss: 16.6859 - val_mean_absolute_error: 3.1300 Epoch 752/1000 7/7 [==============================] - 0s 44ms/step - loss: 1.1284 - mean_absolute_error: 0.5240 - val_loss: 16.5353 - val_mean_absolute_error: 2.9954 Epoch 753/1000 7/7 [==============================] - 0s 25ms/step - loss: 1.4827 - mean_absolute_error: 0.5247 - val_loss: 17.0733 - val_mean_absolute_error: 3.2142 Epoch 754/1000 7/7 [==============================] - 0s 27ms/step - loss: 1.3315 - mean_absolute_error: 0.5905 - val_loss: 16.6210 - val_mean_absolute_error: 3.0680 Epoch 755/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.9816 - mean_absolute_error: 0.5332 - val_loss: 16.9183 - val_mean_absolute_error: 3.1095 Epoch 756/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.7988 - mean_absolute_error: 0.4987 - val_loss: 15.9823 - val_mean_absolute_error: 3.0394 Epoch 757/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.8253 - mean_absolute_error: 0.5266 - val_loss: 17.3986 - val_mean_absolute_error: 3.1729 Epoch 758/1000 7/7 [==============================] - 0s 28ms/step - loss: 1.4954 - mean_absolute_error: 0.6253 - val_loss: 17.4418 - val_mean_absolute_error: 3.1878 Epoch 759/1000 7/7 [==============================] - 0s 27ms/step - loss: 1.0265 - mean_absolute_error: 0.6326 - val_loss: 16.9308 - val_mean_absolute_error: 3.0650 Epoch 760/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.9570 - mean_absolute_error: 0.5970 - val_loss: 18.7164 - val_mean_absolute_error: 3.2489 Epoch 761/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.8733 - mean_absolute_error: 0.6143 - val_loss: 15.3248 - val_mean_absolute_error: 2.9013 Epoch 762/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.7729 - mean_absolute_error: 0.5028 - val_loss: 18.2072 - val_mean_absolute_error: 3.2523 Epoch 763/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.6883 - mean_absolute_error: 0.4927 - val_loss: 16.1708 - val_mean_absolute_error: 3.0331 Epoch 764/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.7253 - mean_absolute_error: 0.4839 - val_loss: 17.6501 - val_mean_absolute_error: 3.2033 Epoch 765/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.4968 - mean_absolute_error: 0.3764 - val_loss: 16.7517 - val_mean_absolute_error: 3.1139 Epoch 766/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.4931 - mean_absolute_error: 0.3218 - val_loss: 16.8897 - val_mean_absolute_error: 3.1356 Epoch 767/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.4711 - mean_absolute_error: 0.3213 - val_loss: 17.0224 - val_mean_absolute_error: 3.1490 Epoch 768/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.4258 - mean_absolute_error: 0.2832 - val_loss: 16.6830 - val_mean_absolute_error: 3.1082 Epoch 769/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.5620 - mean_absolute_error: 0.3508 - val_loss: 18.0041 - val_mean_absolute_error: 3.1736 Epoch 770/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.5856 - mean_absolute_error: 0.3848 - val_loss: 15.8264 - val_mean_absolute_error: 3.0056 Epoch 771/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.4188 - mean_absolute_error: 0.3304 - val_loss: 17.8010 - val_mean_absolute_error: 3.1862 Epoch 772/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.6991 - mean_absolute_error: 0.3743 - val_loss: 16.4545 - val_mean_absolute_error: 3.0290 Epoch 773/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.6584 - mean_absolute_error: 0.4057 - val_loss: 16.9391 - val_mean_absolute_error: 3.1670 Epoch 774/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.4705 - mean_absolute_error: 0.3452 - val_loss: 18.0207 - val_mean_absolute_error: 3.1769 Epoch 775/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.4461 - mean_absolute_error: 0.3352 - val_loss: 16.2401 - val_mean_absolute_error: 3.0146 Epoch 776/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.4787 - mean_absolute_error: 0.3450 - val_loss: 17.1125 - val_mean_absolute_error: 3.1299 Epoch 777/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.5596 - mean_absolute_error: 0.3588 - val_loss: 17.1446 - val_mean_absolute_error: 3.1273 Epoch 778/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.4839 - mean_absolute_error: 0.3405 - val_loss: 16.1851 - val_mean_absolute_error: 3.0262 Epoch 779/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.5038 - mean_absolute_error: 0.3771 - val_loss: 17.0418 - val_mean_absolute_error: 3.1356 Epoch 780/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.5021 - mean_absolute_error: 0.3980 - val_loss: 17.1561 - val_mean_absolute_error: 3.1627 Epoch 781/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.6214 - mean_absolute_error: 0.4128 - val_loss: 16.7890 - val_mean_absolute_error: 3.1269 Epoch 782/1000 7/7 [==============================] - 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loss: 0.5008 - mean_absolute_error: 0.3295 - val_loss: 16.2075 - val_mean_absolute_error: 3.0254 Epoch 789/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.6036 - mean_absolute_error: 0.3620 - val_loss: 16.9148 - val_mean_absolute_error: 3.1036 Epoch 790/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.4525 - mean_absolute_error: 0.3277 - val_loss: 17.4440 - val_mean_absolute_error: 3.1092 Epoch 791/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.8599 - mean_absolute_error: 0.4646 - val_loss: 16.0817 - val_mean_absolute_error: 2.9865 Epoch 792/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.7824 - mean_absolute_error: 0.5515 - val_loss: 16.4711 - val_mean_absolute_error: 3.1184 Epoch 793/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.7829 - mean_absolute_error: 0.5331 - val_loss: 17.5918 - val_mean_absolute_error: 3.1312 Epoch 794/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.5578 - mean_absolute_error: 0.4572 - val_loss: 16.9904 - val_mean_absolute_error: 3.1205 Epoch 795/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.5231 - mean_absolute_error: 0.4337 - val_loss: 15.8604 - val_mean_absolute_error: 2.9907 Epoch 796/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.4681 - mean_absolute_error: 0.3548 - val_loss: 17.2985 - val_mean_absolute_error: 3.1759 Epoch 797/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.4204 - mean_absolute_error: 0.3225 - val_loss: 16.5211 - val_mean_absolute_error: 3.0898 Epoch 798/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.3779 - mean_absolute_error: 0.2833 - val_loss: 17.3466 - val_mean_absolute_error: 3.1707 Epoch 799/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3868 - mean_absolute_error: 0.2614 - val_loss: 16.7227 - val_mean_absolute_error: 3.0478 Epoch 800/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.3639 - mean_absolute_error: 0.2647 - val_loss: 17.4529 - val_mean_absolute_error: 3.1609 Epoch 801/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3364 - mean_absolute_error: 0.2378 - val_loss: 16.8299 - val_mean_absolute_error: 3.0660 Epoch 802/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.3740 - mean_absolute_error: 0.2754 - val_loss: 16.4403 - val_mean_absolute_error: 3.0614 Epoch 803/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.3516 - mean_absolute_error: 0.2674 - val_loss: 16.6605 - val_mean_absolute_error: 3.0695 Epoch 804/1000 7/7 [==============================] - 0s 16ms/step - loss: 0.3200 - mean_absolute_error: 0.2412 - val_loss: 17.2941 - val_mean_absolute_error: 3.2041 Epoch 805/1000 7/7 [==============================] - 0s 16ms/step - loss: 0.2949 - mean_absolute_error: 0.2436 - val_loss: 16.4200 - val_mean_absolute_error: 3.0346 Epoch 806/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.2771 - mean_absolute_error: 0.2354 - val_loss: 16.8091 - val_mean_absolute_error: 3.0985 Epoch 807/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.2908 - mean_absolute_error: 0.2107 - val_loss: 17.2948 - val_mean_absolute_error: 3.1374 Epoch 808/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.2570 - mean_absolute_error: 0.2146 - val_loss: 16.7984 - val_mean_absolute_error: 3.0951 Epoch 809/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.2815 - mean_absolute_error: 0.2557 - val_loss: 16.4608 - val_mean_absolute_error: 3.0856 Epoch 810/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.2441 - mean_absolute_error: 0.2372 - val_loss: 17.0109 - val_mean_absolute_error: 3.0920 Epoch 811/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.2528 - mean_absolute_error: 0.2414 - val_loss: 16.6791 - val_mean_absolute_error: 3.0484 Epoch 812/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.3624 - mean_absolute_error: 0.2585 - val_loss: 16.6394 - val_mean_absolute_error: 3.0769 Epoch 813/1000 7/7 [==============================] - 0s 16ms/step - loss: 0.2108 - mean_absolute_error: 0.2093 - val_loss: 16.8120 - val_mean_absolute_error: 3.0673 Epoch 814/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.2831 - mean_absolute_error: 0.2000 - val_loss: 16.7010 - val_mean_absolute_error: 3.0753 Epoch 815/1000 7/7 [==============================] - 0s 16ms/step - loss: 0.3208 - mean_absolute_error: 0.1917 - val_loss: 16.9013 - val_mean_absolute_error: 3.0792 Epoch 816/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.9965 - mean_absolute_error: 0.3017 - val_loss: 16.2553 - val_mean_absolute_error: 3.0392 Epoch 817/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.9058 - mean_absolute_error: 0.4510 - val_loss: 16.6430 - val_mean_absolute_error: 3.0458 Epoch 818/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.5998 - mean_absolute_error: 0.4775 - val_loss: 16.9559 - val_mean_absolute_error: 3.0897 Epoch 819/1000 7/7 [==============================] - 0s 16ms/step - loss: 0.6228 - mean_absolute_error: 0.4619 - val_loss: 16.7307 - val_mean_absolute_error: 3.0798 Epoch 820/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.7472 - mean_absolute_error: 0.4559 - val_loss: 17.1317 - val_mean_absolute_error: 3.2089 Epoch 821/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.5031 - mean_absolute_error: 0.3836 - val_loss: 16.0880 - val_mean_absolute_error: 3.0165 Epoch 822/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.5764 - mean_absolute_error: 0.4058 - val_loss: 17.0966 - val_mean_absolute_error: 3.1829 Epoch 823/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.5318 - mean_absolute_error: 0.4251 - val_loss: 15.0629 - val_mean_absolute_error: 2.9482 Epoch 824/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.7301 - mean_absolute_error: 0.4597 - val_loss: 17.6050 - val_mean_absolute_error: 3.2588 Epoch 825/1000 7/7 [==============================] - 0s 16ms/step - loss: 0.5157 - mean_absolute_error: 0.4089 - val_loss: 15.5387 - val_mean_absolute_error: 2.9509 Epoch 826/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.5217 - mean_absolute_error: 0.4135 - val_loss: 16.9845 - val_mean_absolute_error: 3.1379 Epoch 827/1000 7/7 [==============================] - 0s 16ms/step - loss: 0.3994 - mean_absolute_error: 0.3506 - val_loss: 16.7572 - val_mean_absolute_error: 3.0764 Epoch 828/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.4356 - mean_absolute_error: 0.3613 - val_loss: 16.9278 - val_mean_absolute_error: 3.0986 Epoch 829/1000 7/7 [==============================] - 0s 16ms/step - loss: 0.5090 - mean_absolute_error: 0.3223 - val_loss: 16.0309 - val_mean_absolute_error: 2.9979 Epoch 830/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.3857 - mean_absolute_error: 0.3191 - val_loss: 16.4230 - val_mean_absolute_error: 3.0529 Epoch 831/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.4877 - mean_absolute_error: 0.2820 - val_loss: 17.0495 - val_mean_absolute_error: 3.1314 Epoch 832/1000 7/7 [==============================] - 0s 16ms/step - loss: 0.2839 - mean_absolute_error: 0.2909 - val_loss: 16.8070 - val_mean_absolute_error: 3.1102 Epoch 833/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.7239 - mean_absolute_error: 0.3802 - val_loss: 15.5443 - val_mean_absolute_error: 2.9464 Epoch 834/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.6751 - mean_absolute_error: 0.3681 - val_loss: 17.6911 - val_mean_absolute_error: 3.1742 Epoch 835/1000 7/7 [==============================] - 0s 16ms/step - loss: 0.4862 - mean_absolute_error: 0.3463 - val_loss: 16.5282 - val_mean_absolute_error: 3.1112 Epoch 836/1000 7/7 [==============================] - 0s 16ms/step - loss: 0.5079 - mean_absolute_error: 0.3507 - val_loss: 17.0116 - val_mean_absolute_error: 3.1619 Epoch 837/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.3725 - mean_absolute_error: 0.3457 - val_loss: 16.2711 - val_mean_absolute_error: 3.0575 Epoch 838/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.3128 - mean_absolute_error: 0.2979 - val_loss: 17.5636 - val_mean_absolute_error: 3.1698 Epoch 839/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2966 - mean_absolute_error: 0.2970 - val_loss: 16.6913 - val_mean_absolute_error: 3.0428 Epoch 840/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.4239 - mean_absolute_error: 0.3028 - val_loss: 16.9201 - val_mean_absolute_error: 3.0806 Epoch 841/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2210 - mean_absolute_error: 0.2675 - val_loss: 15.9851 - val_mean_absolute_error: 3.0192 Epoch 842/1000 7/7 [==============================] - 0s 25ms/step - loss: 1.5071 - mean_absolute_error: 0.3997 - val_loss: 15.8663 - val_mean_absolute_error: 2.9585 Epoch 843/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.8310 - mean_absolute_error: 0.4527 - val_loss: 16.1660 - val_mean_absolute_error: 2.9330 Epoch 844/1000 7/7 [==============================] - 0s 16ms/step - loss: 0.7636 - mean_absolute_error: 0.4950 - val_loss: 15.2947 - val_mean_absolute_error: 2.9090 Epoch 845/1000 7/7 [==============================] - 0s 15ms/step - loss: 0.7324 - mean_absolute_error: 0.5235 - val_loss: 15.3219 - val_mean_absolute_error: 2.9697 Epoch 846/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.7914 - mean_absolute_error: 0.5652 - val_loss: 16.3533 - val_mean_absolute_error: 2.9886 Epoch 847/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.5271 - mean_absolute_error: 0.4691 - val_loss: 16.1399 - val_mean_absolute_error: 3.0532 Epoch 848/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.5444 - mean_absolute_error: 0.4292 - val_loss: 16.9702 - val_mean_absolute_error: 3.0842 Epoch 849/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.7800 - mean_absolute_error: 0.4411 - val_loss: 16.3960 - val_mean_absolute_error: 3.0915 Epoch 850/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.4796 - mean_absolute_error: 0.4545 - val_loss: 16.7383 - val_mean_absolute_error: 3.0672 Epoch 851/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.5816 - mean_absolute_error: 0.3806 - val_loss: 16.4977 - val_mean_absolute_error: 3.0445 Epoch 852/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.5147 - mean_absolute_error: 0.3788 - val_loss: 16.2521 - val_mean_absolute_error: 3.0152 Epoch 853/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.3190 - mean_absolute_error: 0.3371 - val_loss: 17.0910 - val_mean_absolute_error: 3.0962 Epoch 854/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.3305 - mean_absolute_error: 0.3061 - val_loss: 16.3433 - val_mean_absolute_error: 2.9961 Epoch 855/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.2916 - mean_absolute_error: 0.2835 - val_loss: 16.5341 - val_mean_absolute_error: 3.0874 Epoch 856/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.3355 - mean_absolute_error: 0.2779 - val_loss: 16.6149 - val_mean_absolute_error: 3.0889 Epoch 857/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.3433 - mean_absolute_error: 0.2807 - val_loss: 15.9765 - val_mean_absolute_error: 2.9429 Epoch 858/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.3405 - mean_absolute_error: 0.3707 - val_loss: 18.6710 - val_mean_absolute_error: 3.2536 Epoch 859/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.8690 - mean_absolute_error: 0.4332 - val_loss: 15.1244 - val_mean_absolute_error: 2.9030 Epoch 860/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.5902 - mean_absolute_error: 0.4220 - val_loss: 17.2973 - val_mean_absolute_error: 3.1553 Epoch 861/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.4689 - mean_absolute_error: 0.3984 - val_loss: 16.1512 - val_mean_absolute_error: 2.9513 Epoch 862/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.6151 - mean_absolute_error: 0.4026 - val_loss: 16.6813 - val_mean_absolute_error: 3.0648 Epoch 863/1000 7/7 [==============================] - 1s 94ms/step - loss: 0.4913 - mean_absolute_error: 0.3843 - val_loss: 15.9380 - val_mean_absolute_error: 3.0048 Epoch 864/1000 7/7 [==============================] - 1s 85ms/step - loss: 0.5042 - mean_absolute_error: 0.3966 - val_loss: 15.2635 - val_mean_absolute_error: 2.8939 Epoch 865/1000 7/7 [==============================] - 1s 93ms/step - loss: 0.5614 - mean_absolute_error: 0.3515 - val_loss: 16.4453 - val_mean_absolute_error: 3.0314 Epoch 866/1000 7/7 [==============================] - 1s 87ms/step - loss: 0.3732 - mean_absolute_error: 0.3595 - val_loss: 16.5051 - val_mean_absolute_error: 3.0874 Epoch 867/1000 7/7 [==============================] - 0s 70ms/step - loss: 0.3664 - mean_absolute_error: 0.3434 - val_loss: 16.4838 - val_mean_absolute_error: 3.0779 Epoch 868/1000 7/7 [==============================] - 0s 51ms/step - loss: 0.3500 - mean_absolute_error: 0.3513 - val_loss: 15.8274 - val_mean_absolute_error: 2.9632 Epoch 869/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.3084 - mean_absolute_error: 0.3261 - val_loss: 17.0111 - val_mean_absolute_error: 3.1190 Epoch 870/1000 7/7 [==============================] - 0s 43ms/step - loss: 0.2502 - mean_absolute_error: 0.2700 - val_loss: 15.5242 - val_mean_absolute_error: 2.9217 Epoch 871/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.2310 - mean_absolute_error: 0.2368 - val_loss: 17.2143 - val_mean_absolute_error: 3.1343 Epoch 872/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.2005 - mean_absolute_error: 0.2114 - val_loss: 15.6936 - val_mean_absolute_error: 2.9704 Epoch 873/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.2151 - mean_absolute_error: 0.2082 - val_loss: 16.4437 - val_mean_absolute_error: 3.0265 Epoch 874/1000 7/7 [==============================] - 0s 62ms/step - loss: 0.3933 - mean_absolute_error: 0.2098 - val_loss: 16.1393 - val_mean_absolute_error: 3.0231 Epoch 875/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.4383 - mean_absolute_error: 0.2547 - val_loss: 15.7537 - val_mean_absolute_error: 2.9705 Epoch 876/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.2515 - mean_absolute_error: 0.2373 - val_loss: 16.8670 - val_mean_absolute_error: 3.0866 Epoch 877/1000 7/7 [==============================] - 0s 43ms/step - loss: 0.2065 - mean_absolute_error: 0.2121 - val_loss: 15.8240 - val_mean_absolute_error: 2.9913 Epoch 878/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.1933 - mean_absolute_error: 0.2350 - val_loss: 16.7853 - val_mean_absolute_error: 3.0979 Epoch 879/1000 7/7 [==============================] - 0s 47ms/step - loss: 0.3054 - mean_absolute_error: 0.2696 - val_loss: 15.9628 - val_mean_absolute_error: 2.9813 Epoch 880/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.1365 - mean_absolute_error: 0.2062 - val_loss: 16.5022 - val_mean_absolute_error: 3.0541 Epoch 881/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.3353 - mean_absolute_error: 0.2103 - val_loss: 16.3884 - val_mean_absolute_error: 3.0380 Epoch 882/1000 7/7 [==============================] - 0s 69ms/step - loss: 0.1573 - mean_absolute_error: 0.1938 - val_loss: 16.6958 - val_mean_absolute_error: 3.0949 Epoch 883/1000 7/7 [==============================] - 0s 58ms/step - loss: 0.1611 - mean_absolute_error: 0.1913 - val_loss: 15.9188 - val_mean_absolute_error: 2.9602 Epoch 884/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.1909 - mean_absolute_error: 0.2030 - val_loss: 16.4492 - val_mean_absolute_error: 3.0514 Epoch 885/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.1323 - mean_absolute_error: 0.1847 - val_loss: 16.5603 - val_mean_absolute_error: 3.0685 Epoch 886/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.3319 - mean_absolute_error: 0.1947 - val_loss: 16.6487 - val_mean_absolute_error: 3.0438 Epoch 887/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.4226 - mean_absolute_error: 0.2506 - val_loss: 16.4659 - val_mean_absolute_error: 3.0378 Epoch 888/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.1794 - mean_absolute_error: 0.2462 - val_loss: 16.5767 - val_mean_absolute_error: 3.0326 Epoch 889/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.3563 - mean_absolute_error: 0.2534 - val_loss: 16.0225 - val_mean_absolute_error: 3.0028 Epoch 890/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.1960 - mean_absolute_error: 0.2361 - val_loss: 16.3819 - val_mean_absolute_error: 3.0275 Epoch 891/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.3638 - mean_absolute_error: 0.2302 - val_loss: 16.0266 - val_mean_absolute_error: 2.9951 Epoch 892/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.1686 - mean_absolute_error: 0.2480 - val_loss: 16.8881 - val_mean_absolute_error: 3.0900 Epoch 893/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.3208 - mean_absolute_error: 0.2726 - val_loss: 15.9799 - val_mean_absolute_error: 3.0405 Epoch 894/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.1694 - mean_absolute_error: 0.2705 - val_loss: 16.3812 - val_mean_absolute_error: 3.0722 Epoch 895/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.3534 - mean_absolute_error: 0.3493 - val_loss: 15.8059 - val_mean_absolute_error: 2.9497 Epoch 896/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.3081 - mean_absolute_error: 0.3566 - val_loss: 16.1619 - val_mean_absolute_error: 3.0217 Epoch 897/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.2168 - mean_absolute_error: 0.3398 - val_loss: 16.5201 - val_mean_absolute_error: 3.0626 Epoch 898/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.1892 - mean_absolute_error: 0.2784 - val_loss: 16.0677 - val_mean_absolute_error: 3.0148 Epoch 899/1000 7/7 [==============================] - 0s 45ms/step - loss: 1.0688 - mean_absolute_error: 0.3690 - val_loss: 16.8416 - val_mean_absolute_error: 3.0986 Epoch 900/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.8176 - mean_absolute_error: 0.3550 - val_loss: 15.7028 - val_mean_absolute_error: 3.0095 Epoch 901/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.9558 - mean_absolute_error: 0.4451 - val_loss: 16.8735 - val_mean_absolute_error: 3.0871 Epoch 902/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.5490 - mean_absolute_error: 0.3741 - val_loss: 15.4904 - val_mean_absolute_error: 2.9312 Epoch 903/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.4204 - mean_absolute_error: 0.3863 - val_loss: 17.1501 - val_mean_absolute_error: 3.1252 Epoch 904/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.2899 - mean_absolute_error: 0.3155 - val_loss: 16.5139 - val_mean_absolute_error: 3.0616 Epoch 905/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.2770 - mean_absolute_error: 0.2833 - val_loss: 16.3364 - val_mean_absolute_error: 3.0633 Epoch 906/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.2262 - mean_absolute_error: 0.2665 - val_loss: 16.4935 - val_mean_absolute_error: 3.0433 Epoch 907/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.2467 - mean_absolute_error: 0.2450 - val_loss: 16.2684 - val_mean_absolute_error: 3.0238 Epoch 908/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.2118 - mean_absolute_error: 0.2291 - val_loss: 16.4787 - val_mean_absolute_error: 3.0805 Epoch 909/1000 7/7 [==============================] - 0s 44ms/step - loss: 0.1541 - mean_absolute_error: 0.2118 - val_loss: 16.3358 - val_mean_absolute_error: 3.0421 Epoch 910/1000 7/7 [==============================] - 0s 40ms/step - loss: 0.2343 - mean_absolute_error: 0.2154 - val_loss: 16.5201 - val_mean_absolute_error: 3.0334 Epoch 911/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.2543 - mean_absolute_error: 0.2217 - val_loss: 16.2094 - val_mean_absolute_error: 3.0472 Epoch 912/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.2228 - mean_absolute_error: 0.2588 - val_loss: 16.4996 - val_mean_absolute_error: 3.0821 Epoch 913/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.3157 - mean_absolute_error: 0.2954 - val_loss: 16.2902 - val_mean_absolute_error: 3.0306 Epoch 914/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.2002 - mean_absolute_error: 0.2762 - val_loss: 17.0393 - val_mean_absolute_error: 3.0937 Epoch 915/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.3815 - mean_absolute_error: 0.3538 - val_loss: 15.6208 - val_mean_absolute_error: 2.9344 Epoch 916/1000 7/7 [==============================] - 0s 70ms/step - loss: 0.2679 - mean_absolute_error: 0.3102 - val_loss: 17.8737 - val_mean_absolute_error: 3.1886 Epoch 917/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.2368 - mean_absolute_error: 0.3151 - val_loss: 15.5624 - val_mean_absolute_error: 2.9790 Epoch 918/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.2234 - mean_absolute_error: 0.2998 - val_loss: 16.7091 - val_mean_absolute_error: 3.1075 Epoch 919/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.1558 - mean_absolute_error: 0.2458 - val_loss: 16.1445 - val_mean_absolute_error: 2.9871 Epoch 920/1000 7/7 [==============================] - 0s 56ms/step - loss: 0.1602 - mean_absolute_error: 0.2474 - val_loss: 16.5360 - val_mean_absolute_error: 3.0719 Epoch 921/1000 7/7 [==============================] - 0s 42ms/step - loss: 0.1134 - mean_absolute_error: 0.1927 - val_loss: 16.5670 - val_mean_absolute_error: 3.0601 Epoch 922/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0701 - mean_absolute_error: 0.1524 - val_loss: 17.1975 - val_mean_absolute_error: 3.1184 Epoch 923/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.1046 - mean_absolute_error: 0.1638 - val_loss: 15.9177 - val_mean_absolute_error: 2.9984 Epoch 924/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.2177 - mean_absolute_error: 0.2276 - val_loss: 16.9374 - val_mean_absolute_error: 3.1237 Epoch 925/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.2114 - mean_absolute_error: 0.1885 - val_loss: 16.5886 - val_mean_absolute_error: 3.0538 Epoch 926/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.2525 - mean_absolute_error: 0.3162 - val_loss: 16.6724 - val_mean_absolute_error: 3.0806 Epoch 927/1000 7/7 [==============================] - 0s 30ms/step - loss: 1.8441 - mean_absolute_error: 0.4753 - val_loss: 14.9697 - val_mean_absolute_error: 2.8837 Epoch 928/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.7986 - mean_absolute_error: 0.4696 - val_loss: 17.2164 - val_mean_absolute_error: 3.1081 Epoch 929/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.8084 - mean_absolute_error: 0.4510 - val_loss: 15.3010 - val_mean_absolute_error: 2.9060 Epoch 930/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.6941 - mean_absolute_error: 0.4870 - val_loss: 15.7793 - val_mean_absolute_error: 2.9637 Epoch 931/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.5905 - mean_absolute_error: 0.4356 - val_loss: 16.1858 - val_mean_absolute_error: 3.0061 Epoch 932/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.5170 - mean_absolute_error: 0.3726 - val_loss: 15.6890 - val_mean_absolute_error: 2.9793 Epoch 933/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.4691 - mean_absolute_error: 0.3413 - val_loss: 17.2188 - val_mean_absolute_error: 3.1444 Epoch 934/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.4607 - mean_absolute_error: 0.3030 - val_loss: 15.6031 - val_mean_absolute_error: 2.9255 Epoch 935/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.3966 - mean_absolute_error: 0.3156 - val_loss: 17.0126 - val_mean_absolute_error: 3.0854 Epoch 936/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.4489 - mean_absolute_error: 0.3202 - val_loss: 15.4230 - val_mean_absolute_error: 2.9044 Epoch 937/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.4496 - mean_absolute_error: 0.2685 - val_loss: 16.5447 - val_mean_absolute_error: 3.0499 Epoch 938/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.3795 - mean_absolute_error: 0.2791 - val_loss: 15.9073 - val_mean_absolute_error: 2.9345 Epoch 939/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.4032 - mean_absolute_error: 0.2740 - val_loss: 16.2865 - val_mean_absolute_error: 3.0159 Epoch 940/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.3793 - mean_absolute_error: 0.2810 - val_loss: 16.8133 - val_mean_absolute_error: 3.0707 Epoch 941/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.3859 - mean_absolute_error: 0.2764 - val_loss: 15.5632 - val_mean_absolute_error: 2.9195 Epoch 942/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.4135 - mean_absolute_error: 0.2840 - val_loss: 16.2462 - val_mean_absolute_error: 3.0229 Epoch 943/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.3569 - mean_absolute_error: 0.2987 - val_loss: 16.5680 - val_mean_absolute_error: 3.0543 Epoch 944/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.4217 - mean_absolute_error: 0.2946 - val_loss: 16.4220 - val_mean_absolute_error: 3.0254 Epoch 945/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.3242 - mean_absolute_error: 0.2766 - val_loss: 16.4342 - val_mean_absolute_error: 3.0446 Epoch 946/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.3035 - mean_absolute_error: 0.2598 - val_loss: 15.8009 - val_mean_absolute_error: 2.9448 Epoch 947/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.3266 - mean_absolute_error: 0.2504 - val_loss: 16.7070 - val_mean_absolute_error: 3.0545 Epoch 948/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.3037 - mean_absolute_error: 0.2561 - val_loss: 16.1435 - val_mean_absolute_error: 3.0285 Epoch 949/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.3396 - mean_absolute_error: 0.2813 - val_loss: 16.5566 - val_mean_absolute_error: 3.0233 Epoch 950/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.2776 - mean_absolute_error: 0.2595 - val_loss: 16.9565 - val_mean_absolute_error: 3.1382 Epoch 951/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.3708 - mean_absolute_error: 0.2547 - val_loss: 16.3418 - val_mean_absolute_error: 3.0038 Epoch 952/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.4073 - mean_absolute_error: 0.2725 - val_loss: 16.7625 - val_mean_absolute_error: 3.0589 Epoch 953/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.3007 - mean_absolute_error: 0.2776 - val_loss: 16.5890 - val_mean_absolute_error: 3.0331 Epoch 954/1000 7/7 [==============================] - 0s 46ms/step - loss: 0.3816 - mean_absolute_error: 0.2950 - val_loss: 16.4269 - val_mean_absolute_error: 3.0328 Epoch 955/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.2385 - mean_absolute_error: 0.2329 - val_loss: 15.8832 - val_mean_absolute_error: 2.9691 Epoch 956/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2999 - mean_absolute_error: 0.2521 - val_loss: 16.3888 - val_mean_absolute_error: 3.0274 Epoch 957/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.2933 - mean_absolute_error: 0.2733 - val_loss: 16.9306 - val_mean_absolute_error: 3.1205 Epoch 958/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.3004 - mean_absolute_error: 0.3369 - val_loss: 15.9031 - val_mean_absolute_error: 2.9639 Epoch 959/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3458 - mean_absolute_error: 0.3459 - val_loss: 17.7130 - val_mean_absolute_error: 3.1481 Epoch 960/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.2344 - mean_absolute_error: 0.2810 - val_loss: 15.8587 - val_mean_absolute_error: 2.9774 Epoch 961/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3959 - mean_absolute_error: 0.3015 - val_loss: 16.9763 - val_mean_absolute_error: 3.0815 Epoch 962/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.3965 - mean_absolute_error: 0.3178 - val_loss: 16.5615 - val_mean_absolute_error: 3.0415 Epoch 963/1000 7/7 [==============================] - 0s 44ms/step - loss: 0.4812 - mean_absolute_error: 0.3978 - val_loss: 15.7042 - val_mean_absolute_error: 2.9946 Epoch 964/1000 7/7 [==============================] - 0s 47ms/step - loss: 1.1333 - mean_absolute_error: 0.4753 - val_loss: 15.1434 - val_mean_absolute_error: 2.9031 Epoch 965/1000 7/7 [==============================] - 1s 100ms/step - loss: 0.8940 - mean_absolute_error: 0.5908 - val_loss: 15.6628 - val_mean_absolute_error: 2.9999 Epoch 966/1000 7/7 [==============================] - 0s 36ms/step - loss: 1.0274 - mean_absolute_error: 0.5749 - val_loss: 16.6916 - val_mean_absolute_error: 3.0562 Epoch 967/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.7871 - mean_absolute_error: 0.5942 - val_loss: 17.4787 - val_mean_absolute_error: 3.1418 Epoch 968/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.9221 - mean_absolute_error: 0.6461 - val_loss: 13.8977 - val_mean_absolute_error: 2.9019 Epoch 969/1000 7/7 [==============================] - 0s 40ms/step - loss: 0.6901 - mean_absolute_error: 0.5455 - val_loss: 17.7516 - val_mean_absolute_error: 3.2281 Epoch 970/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.6825 - mean_absolute_error: 0.5035 - val_loss: 15.2090 - val_mean_absolute_error: 2.9340 Epoch 971/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.7717 - mean_absolute_error: 0.5276 - val_loss: 17.6725 - val_mean_absolute_error: 3.2706 Epoch 972/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.6862 - mean_absolute_error: 0.5495 - val_loss: 14.0100 - val_mean_absolute_error: 2.8631 Epoch 973/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.6007 - mean_absolute_error: 0.4659 - val_loss: 16.3712 - val_mean_absolute_error: 3.0651 Epoch 974/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.5347 - mean_absolute_error: 0.4748 - val_loss: 15.9488 - val_mean_absolute_error: 3.0015 Epoch 975/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.4561 - mean_absolute_error: 0.4542 - val_loss: 16.1102 - val_mean_absolute_error: 3.0590 Epoch 976/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.4151 - mean_absolute_error: 0.4083 - val_loss: 15.6106 - val_mean_absolute_error: 2.9765 Epoch 977/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.3559 - mean_absolute_error: 0.3546 - val_loss: 15.1187 - val_mean_absolute_error: 2.9351 Epoch 978/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.5056 - mean_absolute_error: 0.3633 - val_loss: 15.3024 - val_mean_absolute_error: 2.9762 Epoch 979/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.4641 - mean_absolute_error: 0.3032 - val_loss: 15.2888 - val_mean_absolute_error: 2.9401 Epoch 980/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.3288 - mean_absolute_error: 0.3205 - val_loss: 15.5860 - val_mean_absolute_error: 3.0349 Epoch 981/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.2343 - mean_absolute_error: 0.2884 - val_loss: 15.4094 - val_mean_absolute_error: 2.9619 Epoch 982/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.2706 - mean_absolute_error: 0.2715 - val_loss: 15.7896 - val_mean_absolute_error: 2.9778 Epoch 983/1000 7/7 [==============================] - 0s 16ms/step - loss: 0.2239 - mean_absolute_error: 0.2583 - val_loss: 15.2216 - val_mean_absolute_error: 2.9533 Epoch 984/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.2817 - mean_absolute_error: 0.2749 - val_loss: 15.9504 - val_mean_absolute_error: 3.0332 Epoch 985/1000 7/7 [==============================] - 0s 17ms/step - loss: 0.2452 - mean_absolute_error: 0.2463 - val_loss: 15.2078 - val_mean_absolute_error: 2.9103 Epoch 986/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.2031 - mean_absolute_error: 0.2434 - val_loss: 15.6695 - val_mean_absolute_error: 3.0054 Epoch 987/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.1530 - mean_absolute_error: 0.2108 - val_loss: 14.9451 - val_mean_absolute_error: 2.9230 Epoch 988/1000 7/7 [==============================] - 0s 16ms/step - loss: 0.1957 - mean_absolute_error: 0.2382 - val_loss: 16.3585 - val_mean_absolute_error: 3.0289 Epoch 989/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1827 - mean_absolute_error: 0.2244 - val_loss: 15.4591 - val_mean_absolute_error: 2.9384 Epoch 990/1000 7/7 [==============================] - 0s 45ms/step - loss: 0.1796 - mean_absolute_error: 0.2207 - val_loss: 15.5358 - val_mean_absolute_error: 2.9600 Epoch 991/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.1566 - mean_absolute_error: 0.2052 - val_loss: 15.9439 - val_mean_absolute_error: 3.0195 Epoch 992/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.1426 - mean_absolute_error: 0.1896 - val_loss: 15.0004 - val_mean_absolute_error: 2.9031 Epoch 993/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.2500 - mean_absolute_error: 0.2430 - val_loss: 16.6894 - val_mean_absolute_error: 3.0880 Epoch 994/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.1734 - mean_absolute_error: 0.2519 - val_loss: 15.3132 - val_mean_absolute_error: 2.9697 Epoch 995/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.1838 - mean_absolute_error: 0.2790 - val_loss: 15.8178 - val_mean_absolute_error: 3.0270 Epoch 996/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.2270 - mean_absolute_error: 0.2889 - val_loss: 15.5242 - val_mean_absolute_error: 2.9228 Epoch 997/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.1715 - mean_absolute_error: 0.2477 - val_loss: 15.9188 - val_mean_absolute_error: 3.0495 Epoch 998/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.1364 - mean_absolute_error: 0.2357 - val_loss: 14.9894 - val_mean_absolute_error: 2.9027 Epoch 999/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.1325 - mean_absolute_error: 0.2503 - val_loss: 17.0825 - val_mean_absolute_error: 3.0966 Epoch 1000/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.1311 - mean_absolute_error: 0.2674 - val_loss: 15.2783 - val_mean_absolute_error: 2.9224 7/7 [==============================] - 1s 4ms/step 1/1 [==============================] - 0s 21ms/step 1/1 [==============================] - 0s 20ms/step
# Calculate the RMSE
rmse_lstm = sqrt(mean_squared_error(y_test_F, test_predictions_F))
print('The RMSE value of LSTM model (FORD): {:.4f}'.format(rmse_lstm))
The RMSE value of LSTM model (FORD): 3.2206
# Plot Training Observations VS Training Predictions
figure(figsize=(20, 10), dpi=100)
plt.plot(dates_train_F, train_predictions_F)
plt.plot(dates_train_F, y_train_F)
plt.title('LSTM: Training Actual Returns/Training Predicted Returns (FORD)', fontsize=16)
plt.legend(['FORD Training Predictions', 'FORD Training Observations'])
# Plot Testing Observations VS Testing Predictions
figure(figsize=(20, 10), dpi=100)
plt.plot(dates_test_F, test_predictions_F)
plt.plot(dates_test_F, y_test_F)
plt.title('LSTM: Testing Actual Returns/Testing Predicted Returns (FORD)', fontsize=16)
plt.legend(['FORD Testing Predictions', 'FORD Testing Observations'])
# General Plot (Training, Validation & testing)
figure(figsize=(20, 10), dpi=100)
plt.plot(dates_train_F, train_predictions_F)
plt.plot(dates_train_F, y_train_F)
plt.plot(dates_val_F, val_predictions_F)
plt.plot(dates_val_F, y_val_F)
plt.plot(dates_test_F, test_predictions_F)
plt.plot(dates_test_F, y_test_F)
plt.title('LSTM: General forecasting plot (FORD)', fontsize=16)
plt.legend(['FORD Training Predictions',
'FORD Training Observations',
'FORD Validation Predictions',
'FORD Validation Observations',
'FORD Testing Predictions',
'FORD Testing Observations'])
<matplotlib.legend.Legend at 0x7fa8ad581f70>
# transform a time series dataset into a supervised learning dataset
def series_to_supervised(DBK_RET, n_in=1, n_out=1, dropnan=True):
n_vars = 1 if type(DBK_RET) is list else DBK_RET.shape[1]
df = DataFrame(DBK_RET)
cols = list()
# input sequence (t-n, ... t-1)
for i in range(n_in, 0, -1):
cols.append(df.shift(i))
# forecast sequence (t, t+1, ... t+n)
for i in range(0, n_out):
cols.append(df.shift(-i))
# put it all together
agg = concat(cols, axis=1)
# drop rows with NaN values
if dropnan:
agg.dropna(inplace=True)
return agg.values
# split a univariate dataset into train/test sets
def train_test_split(DBK_RET, n_test):
return DBK_RET[:-n_test, :], DBK_RET[-n_test:, :]
# fit an random forest model and make a one step prediction
def random_forest_forecast(train, testX):
# transform list into array
train = asarray(train)
# split into input and output columns
trainX, trainy = train[:, :-1], train[:, -1]
# fit model
model = RandomForestRegressor()
model.fit(trainX, trainy)
# make a one-step prediction
yhat = model.predict([testX])
return yhat[0]
# walk-forward validation for univariate data
def walk_forward_validation(DBK_RET, n_test):
predictions = list()
# split dataset
train, test = train_test_split(DBK_RET, n_test)
# seed history with training dataset
history = [x for x in train]
# step over each time-step in the test set
for i in range(len(test)):
# split test row into input and output columns
testX, testy = test[i, :-1], test[i, -1]
# fit model on history and make a prediction
yhat = random_forest_forecast(history, testX)
# store forecast in list of predictions
predictions.append(yhat)
# add actual observation to history for the next loop
history.append(test[i])
# summarize progress
print('>expected=%.1f, predicted=%.1f' % (testy, yhat))
# estimate prediction error
error = mean_absolute_error(test[:, -1], predictions)
return error, test[:, -1], predictions
# transform the time series data into supervised learning
DBK_RET_sl = series_to_supervised(DBK_RET, n_in=6)
# Forecasting & evaluation
mae, y, yhat = walk_forward_validation(DBK_RET_sl, 12)
print('MAE: %.4f' % mae)
>expected=-2.1, predicted=-0.2 >expected=-3.2, predicted=0.1 >expected=-0.2, predicted=0.1 >expected=1.4, predicted=-0.5 >expected=0.6, predicted=0.2 >expected=1.4, predicted=-0.5 >expected=-0.7, predicted=-0.2 >expected=-4.5, predicted=-0.8 >expected=1.8, predicted=0.3 >expected=2.2, predicted=-0.2 >expected=-3.0, predicted=-0.8 >expected=0.1, predicted=-0.2 MAE: 1.7133
# Calculate the RMSE
rmse_rf = sqrt(mean_squared_error(y, yhat))
print('The RMSE value of Random Forest model (DBK): {:.4f}'.format(rmse_rf))
The RMSE value of Random Forest model (DBK): 2.0383
#plot expected vs predicted
figure(figsize=(20, 7), dpi=100)
pyplot.plot(y, label='Actual returns')
pyplot.plot(yhat, label='Predicted retuns')
pyplot.legend()
plt.title('Random forest: Actual / Predicted Returns (DBK)', fontsize=16)
pyplot.show()
# transform a time series dataset into a supervised learning dataset
def series_to_supervised(BAC_RET, n_in=1, n_out=1, dropnan=True):
n_vars = 1 if type(BAC_RET) is list else BAC_RET.shape[1]
df = DataFrame(BAC_RET)
cols = list()
# input sequence (t-n, ... t-1)
for i in range(n_in, 0, -1):
cols.append(df.shift(i))
# forecast sequence (t, t+1, ... t+n)
for i in range(0, n_out):
cols.append(df.shift(-i))
# put it all together
agg = concat(cols, axis=1)
# drop rows with NaN values
if dropnan:
agg.dropna(inplace=True)
return agg.values
# split a univariate dataset into train/test sets
def train_test_split(BAC_RET, n_test):
return BAC_RET[:-n_test, :], BAC_RET[-n_test:, :]
# fit an random forest model and make a one step prediction
def random_forest_forecast(train, testX):
# transform list into array
train = asarray(train)
# split into input and output columns
trainX, trainy = train[:, :-1], train[:, -1]
# fit model
model = RandomForestRegressor()
model.fit(trainX, trainy)
# make a one-step prediction
yhat = model.predict([testX])
return yhat[0]
# walk-forward validation for univariate data
def walk_forward_validation(BAC_RET, n_test):
predictions = list()
# split dataset
train, test = train_test_split(BAC_RET, n_test)
# seed history with training dataset
history = [x for x in train]
# step over each time-step in the test set
for i in range(len(test)):
# split test row into input and output columns
testX, testy = test[i, :-1], test[i, -1]
# fit model on history and make a prediction
yhat = random_forest_forecast(history, testX)
# store forecast in list of predictions
predictions.append(yhat)
# add actual observation to history for the next loop
history.append(test[i])
# summarize progress
print('>expected=%.1f, predicted=%.1f' % (testy, yhat))
# estimate prediction error
error = mean_absolute_error(test[:, -1], predictions)
return error, test[:, -1], predictions
# transform the time series data into supervised learning
BAC_RET_sl = series_to_supervised(BAC_RET, n_in=6)
# Forecasting & evaluation
mae, y, yhat = walk_forward_validation(BAC_RET_sl, 12)
print('MAE: %.4f' % mae)
>expected=1.1, predicted=-0.0 >expected=-1.9, predicted=-0.1 >expected=-1.2, predicted=0.3 >expected=1.5, predicted=-0.1 >expected=0.3, predicted=0.2 >expected=0.4, predicted=-0.2 >expected=-0.6, predicted=0.1 >expected=3.7, predicted=0.3 >expected=-1.8, predicted=-0.6 >expected=2.8, predicted=0.2 >expected=-0.4, predicted=-0.3 >expected=-0.1, predicted=-0.2 MAE: 1.2279
# Calculate the RMSE
rmse_rf = sqrt(mean_squared_error(y, yhat))
print('The RMSE value of Random Forest model (BAC): {:.4f}'.format(rmse_rf))
The RMSE value of Random Forest model (BAC): 1.5683
#plot expected vs predicted
figure(figsize=(20, 7), dpi=100)
pyplot.plot(y, label='Actual returns')
pyplot.plot(yhat, label='Predicted retuns')
pyplot.legend()
plt.title('Random forest: Actual / Predicted Returns (BAC)', fontsize=16)
pyplot.show()
# transform a time series dataset into a supervised learning dataset
def series_to_supervised(BMW_RET, n_in=1, n_out=1, dropnan=True):
n_vars = 1 if type(BMW_RET) is list else BMW_RET.shape[1]
df = DataFrame(BMW_RET)
cols = list()
# input sequence (t-n, ... t-1)
for i in range(n_in, 0, -1):
cols.append(df.shift(i))
# forecast sequence (t, t+1, ... t+n)
for i in range(0, n_out):
cols.append(df.shift(-i))
# put it all together
agg = concat(cols, axis=1)
# drop rows with NaN values
if dropnan:
agg.dropna(inplace=True)
return agg.values
# split a univariate dataset into train/test sets
def train_test_split(BMW_RET, n_test):
return BMW_RET[:-n_test, :], BMW_RET[-n_test:, :]
# fit an random forest model and make a one step prediction
def random_forest_forecast(train, testX):
# transform list into array
train = asarray(train)
# split into input and output columns
trainX, trainy = train[:, :-1], train[:, -1]
# fit model
model = RandomForestRegressor()
model.fit(trainX, trainy)
# make a one-step prediction
yhat = model.predict([testX])
return yhat[0]
# walk-forward validation for univariate data
def walk_forward_validation(BMW_RET, n_test):
predictions = list()
# split dataset
train, test = train_test_split(BMW_RET, n_test)
# seed history with training dataset
history = [x for x in train]
# step over each time-step in the test set
for i in range(len(test)):
# split test row into input and output columns
testX, testy = test[i, :-1], test[i, -1]
# fit model on history and make a prediction
yhat = random_forest_forecast(history, testX)
# store forecast in list of predictions
predictions.append(yhat)
# add actual observation to history for the next loop
history.append(test[i])
# summarize progress
print('>expected=%.1f, predicted=%.1f' % (testy, yhat))
# estimate prediction error
error = mean_absolute_error(test[:, -1], predictions)
return error, test[:, -1], predictions
# transform the time series data into supervised learning
BMW_RET_sl = series_to_supervised(BMW_RET, n_in=6)
# Forecasting & evaluation
mae, y, yhat = walk_forward_validation(BMW_RET_sl, 12)
print('MAE: %.4f' % mae)
>expected=-2.5, predicted=-0.0 >expected=-1.2, predicted=-0.6 >expected=2.0, predicted=0.5 >expected=2.7, predicted=0.0 >expected=0.8, predicted=-0.7 >expected=-0.9, predicted=-0.1 >expected=0.6, predicted=-0.9 >expected=-3.0, predicted=0.6 >expected=0.2, predicted=-0.5 >expected=1.8, predicted=-0.4 >expected=-0.5, predicted=-0.1 >expected=-1.3, predicted=0.1 MAE: 1.5796
# Calculate the RMSE
rmse_rf = sqrt(mean_squared_error(y, yhat))
print('The RMSE value of Random Forest model (BMW): {:.4f}'.format(rmse_rf))
The RMSE value of Random Forest model (BMW): 1.8348
#plot expected vs predicted
figure(figsize=(20, 7), dpi=100)
pyplot.plot(y, label='Actual returns')
pyplot.plot(yhat, label='Predicted retuns')
pyplot.legend()
plt.title('Random forest: Actual / Predicted Returns (BMW)', fontsize=16)
pyplot.show()
# transform a time series dataset into a supervised learning dataset
def series_to_supervised(F_RET, n_in=1, n_out=1, dropnan=True):
n_vars = 1 if type(F_RET) is list else F_RET.shape[1]
df = DataFrame(F_RET)
cols = list()
# input sequence (t-n, ... t-1)
for i in range(n_in, 0, -1):
cols.append(df.shift(i))
# forecast sequence (t, t+1, ... t+n)
for i in range(0, n_out):
cols.append(df.shift(-i))
# put it all together
agg = concat(cols, axis=1)
# drop rows with NaN values
if dropnan:
agg.dropna(inplace=True)
return agg.values
# split a univariate dataset into train/test sets
def train_test_split(F_RET, n_test):
return F_RET[:-n_test, :], F_RET[-n_test:, :]
# fit an random forest model and make a one step prediction
def random_forest_forecast(train, testX):
# transform list into array
train = asarray(train)
# split into input and output columns
trainX, trainy = train[:, :-1], train[:, -1]
# fit model
model = RandomForestRegressor()
model.fit(trainX, trainy)
# make a one-step prediction
yhat = model.predict([testX])
return yhat[0]
# walk-forward validation for univariate data
def walk_forward_validation(F_RET, n_test):
predictions = list()
# split dataset
train, test = train_test_split(F_RET, n_test)
# seed history with training dataset
history = [x for x in train]
# step over each time-step in the test set
for i in range(len(test)):
# split test row into input and output columns
testX, testy = test[i, :-1], test[i, -1]
# fit model on history and make a prediction
yhat = random_forest_forecast(history, testX)
# store forecast in list of predictions
predictions.append(yhat)
# add actual observation to history for the next loop
history.append(test[i])
# summarize progress
print('>expected=%.1f, predicted=%.1f' % (testy, yhat))
# estimate prediction error
error = mean_absolute_error(test[:, -1], predictions)
return error, test[:, -1], predictions
# transform the time series data into supervised learning
F_RET_sl = series_to_supervised(F_RET, n_in=6)
# Forecasting & evaluation
mae, y, yhat = walk_forward_validation(F_RET_sl, 12)
print('MAE: %.4f' % mae)
>expected=-3.6, predicted=-0.9 >expected=-1.1, predicted=-0.6 >expected=-1.2, predicted=-0.7 >expected=2.7, predicted=-0.6 >expected=-1.2, predicted=-0.2 >expected=0.4, predicted=-0.2 >expected=-1.4, predicted=-0.2 >expected=-0.2, predicted=-0.2 >expected=-1.6, predicted=0.2 >expected=2.2, predicted=-0.4 >expected=-0.8, predicted=-0.3 >expected=0.5, predicted=0.2 MAE: 1.2592
# Calculate the RMSE
rmse_rf = sqrt(mean_squared_error(y, yhat))
print('The RMSE value of Random Forest model (FORD): {:.4f}'.format(rmse_rf))
The RMSE value of Random Forest model (FORD): 1.6323
#plot expected vs predicted
figure(figsize=(20, 7), dpi=100)
pyplot.plot(y, label='Actual returns')
pyplot.plot(yhat, label='Predicted retuns')
pyplot.legend()
plt.title('Random forest: Actual / Predicted Returns (FORD)', fontsize=16)
pyplot.show()
# Compare the RMSE of the models
## create data
data_RMSE = [["ARCH", 0.2504,0.2273, 0.2005 ,0.2219 ],
["GARCH", 0.2076,0.1982, 0.1828 , 0.2174],
["GARCH-t", 0.2074,0.2018, 0.1860 , 0.2236],
["EGARCH", 0.2161,0.1997, 0.1836 , 0.2243],
["GJR-GARCH", 0.2072,0.1787, 0.1722 , 0.2156],
["LSTM", 2.6075 ,1.8953, 2.5942 , 3.2206],
["Random Forest", 2.0383,1.5683, 1.8348 , 1.6323]
]
## define header names
col_names = ["Models", "RMSE_DBK","RMSE_BAC", "RMSE_BMW", "RMSE_FORD"]
## display table
print(tabulate(data_RMSE, headers=col_names, tablefmt="fancy_grid"))
╒═══════════════╤════════════╤════════════╤════════════╤═════════════╕ │ Models │ RMSE_DBK │ RMSE_BAC │ RMSE_BMW │ RMSE_FORD │ ╞═══════════════╪════════════╪════════════╪════════════╪═════════════╡ │ ARCH │ 0.2504 │ 0.2273 │ 0.2005 │ 0.2219 │ ├───────────────┼────────────┼────────────┼────────────┼─────────────┤ │ GARCH │ 0.2076 │ 0.1982 │ 0.1828 │ 0.2174 │ ├───────────────┼────────────┼────────────┼────────────┼─────────────┤ │ GARCH-t │ 0.2074 │ 0.2018 │ 0.186 │ 0.2236 │ ├───────────────┼────────────┼────────────┼────────────┼─────────────┤ │ EGARCH │ 0.2161 │ 0.1997 │ 0.1836 │ 0.2243 │ ├───────────────┼────────────┼────────────┼────────────┼─────────────┤ │ GJR-GARCH │ 0.2072 │ 0.1787 │ 0.1722 │ 0.2156 │ ├───────────────┼────────────┼────────────┼────────────┼─────────────┤ │ LSTM │ 2.6075 │ 1.8953 │ 2.5942 │ 3.2206 │ ├───────────────┼────────────┼────────────┼────────────┼─────────────┤ │ Random Forest │ 2.0383 │ 1.5683 │ 1.8348 │ 1.6323 │ ╘═══════════════╧════════════╧════════════╧════════════╧═════════════╛
For each company, we designed and applied GARCH, LTSM, and Random Forest models to demonstrate their importance in forecasting the volatility of these markets. Based on the forecasting results obtained, we can conclude that GARCH models performed much better than Data Science models. Indeed, the best model performance was from GJR GARCH by giving the lowest RMSE and LSTM had the worst performance in both industries.